esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Modeling: Mass-Spring Waveguides
### Coupled Two-Chain Mass-Spring Waveguides  
The discrete elastic equations of motion of the coupled two-chain mass-spring waveguides are:  
\[m\ddot{u}_{n}-k_{m}(u_{n+1}-2u_{n}+u_{n-1})-k_{c}(v_{n}-u_{n})+ \eta\dot{u}_{n} =0, \tag{21}\] \[m\ddot{v}_{n}-k_{m}(v_{n+1}-2v_{n}+v_{n-1})-k_{c}(u_{n}-v_{n})+ \eta\dot{v}_{n} =0 \tag{22}\]  
In Equations (21) and (22), \(u_{n}\) and \(v_{n}\) are the displacements of \(n^{th}\) mass of chain 1 and 2, respectively. The term \(k_{c}\) describes the stiffness of the springs that couples the chains. To model the excitation applied by the transducer, the prescribed base periodic excitation \(F^{u}(t)=F^{u}_{0}\sum_{\omega_{i}}\sin(\omega_{i}t)\) and \(F^{v}(t)=F^{v}_{0}\sum_{\omega_{i}}\sin(\omega_{i}t+\phi^{v}_{0})\) are applied to the first mass of each chain, where \(F^{v}_{0}\) and \(\phi^{v}_{0}\) are the amplitude and phase of the applied force to chain 2.  
In the numerical experiment, first we excite the coupled chains with the first eigen vector (Mode: 1 1) to the first mass of each chain. In Figure 8a, we show the normalized power spectral density of the last mass displacement time series of the first chain for this symmetric mode. The resultant dispersion relation of the symmetric band is shown in Figure 8b. We then excite the coupled chains with the second eigen vector (Mode: 1 -1). To obtain a model which behavior mimics that of the experimental system, we vary the stiffness of the springs that couples the chains, i.e., \(k_{c}\) to obtain a cutoff frequency close to 20.24 kHz (see Figure 4). In Figure 8a, we show the normalized power spectral density of the last mass displacement time series of the first chain for the anti-symmetric mode (Mode: 1 1) for \(k_{c}=33.5\times 10^{6}\) N/m. This second spatial eigen mode (Mode 1 -1: blue of Figure 8a) exhibits a spectrum that differs from that of the symmetric one. This spectrum shows negligible transmission amplitude below the cutoff frequency. Above the cutoff frequency, the transmission spectrum possesses clear resonances with non-uniform frequency spacing, though at low frequency the resonances are closely spaced. Since the wave numbers for the second band are also multiples of  
Figure 7: (**a**) Power spectral density of the of the displacement of the \(N_{m}\) mass showing the eigen frequencies of the single chain mass-spring waveguides. (**b**,**c**) Spatial waveforms of the displacements of all masses for driving frequency of 3884 kHz and 7767 kHz. Two snapshots of the spatial waveforms are shown for time \(t_{1}\) and \(t_{2}\). (**d**) Band structure determined and calculated from (**a**–**c**). System parameters: \(N_{m}=48\), \(\eta=6.08\) Ns/m, \(F^{u}_{0}=1\) N, \(\omega_{i}\in(0,60\) kHz]\).  
\(2N_{m}a\), the corresponding dispersion relation can be calculated using the expression \(\omega_{k}^{2}=\left(\beta k\right)^{2}+2(\alpha)^{2}\). The _lsqcurvefit_ function of MATLAB is used to numerically obtain the cutoff frequency of 19.91 kHz.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 4 Results
### Results
#### 4.2.2 Heterogeneous Media  
Heterogeneity occurs when the object to be imaged has a complex composition. An example is the transcranial PAT where the sound speed in the skull is 3200 m/s in contrast to 1480 m/s in the water. The speed has jump singularities at the interface of the two constituents. Such singularities can be mingled with those from the initial pressure and appear in the reconstructed image as additional artifacts, casting more challenges for the reconstruction of the initial pressure. We implemented the algorithms in the domain \([-1,1]\times[-1,1]\) and chose the distribution of the sound speed as  
\[c=1-0.2\sin(2\pi x)+0.15\cos(\pi y)+\chi_{(x-0.5)^{2}+(y-0.5)^{2}<0.01}, \tag{8}\]  
where \(\chi_{(x-0.5)^{2}+(y-0.5)^{2}<0.01}\) is a function that equals 1 on the disk \(\{(x,y):(x-0.5)^{2}+(y-0.5)^{2}<0.01\}\) and 0 otherwise. The reason for such choice of \(c\) is as follows. The constant 1 models the speed in soft tissue. The smooth term \(-0.2\sin(2\pi x)+0.15\cos(\pi y)\) is added to mimic the slight variation of the sound speed in distinct types of tissue. The non-smooth term \(\chi_{(x-0.5)^{2}+(y-0.5)^{2}<0.01}\) captures the jumps between different materials such as soft tissue and bones; see Figure 5 for the distribution of the sound speed \(c\).  
Figure 4: Reconstruction in a homogeneous medium.  
With the variable sound speed, we computed the PSNR and SSIM of the reconstructed images with 0%, 10%, 20%, 30%, 40% noise added to the ultrasound signals respectively, as is shown in Figure 6. The DL algorithm still demonstrates the optimal overall performance. Besides being more stable and time-saving, the DL algorithm also yields the least outliers. Here an outlier is a number in the dataset that is less than \(Q_{1}-1.5\times(Q_{3}-Q_{1})\) or greater than \(Q_{3}+1.5\times(Q_{3}-Q_{1})\), where \(Q_{1}\) is the lower quartile, and \(Q_{3}\) is the upper quartile.  
Some reconstructed images are randomly selected from the testing set again to illustrate the difference of the algorithms; see Figure 7. ATR still suffers from the high noise level, but can resolve the jumps in the speed. Landweber iteration, however, introduces additional artifacts on the top right of the reconstructed image where the sound speed jumps as shown in Figure 5. This is partly due to the limited number of iterations, and it is observed that artifact becomes weaker if the number of iterations is increased. The DL reconstruction resolves simultaneously the high noise and jumps in the speed. It remains visually the closest to the true initial source.
## 5 Numerical Examples
### Uncertainty Quantification for Elastic Parameters of T300/QY8911 Laminates
Furthermore, by making use of the proposed non-probabilistic quantification method and the aforementioned uncertainty quantification approach with experimental points, two kinds of probability distribution plots are plotted in Figure 6, where the black points are the experimental data and the red line is the fitted probability distribution plot. The blue line is the probability distribution plot via the proposed non-probabilistic method on the basis of micro-macro-mechanical analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**T300/QY8911 Laminates (MPa)**} \\ \hline
65.09 & 62.43 & 60.81 & 60.98 & 62.03 \\
68.19 & 57.32 & 63.49 & 62.72 & 59.54 \\
59.56 & 60.50 & 62.93 & 56.39 & 60.57 \\
63.25 & 65.64 & 59.77 & 56.79 & 61.95 \\
61.51 & 64.99 & 58.79 & 58.79 & 58.12 \\
58.34 & 63.92 & 55.68 & 60.91 & 60.58 \\
59.95 & 60.89 & 57.47 & 62.72 & 62.33 \\
59.04 & 57.95 & 59.95 & 60.62 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental points of longitudinal tension modulus. Figure 6: Probability distribution plots of tension modulus of T300/QY8911 laminates. By utilizing the proposed non-probabilistic quantification method, the obtained nominal value and standard deviation are 60.16 MPa and 3.06 MPa, respectively. The mean value and standard deviation by virtue of experimental data are 60.83 MPa and 2.72 MPa, respectively. That is to say, the proposed method can achieve a very approximate identical result to experimental studies, but the quantified dispersion may be larger. In addition, the simulated minimum and maximum values by the proposed method are 52.12 MPa and 68.65 MPa, which can cover the minimum and maximum values by the experiments, namely 55.68 MPa and 68.19 MPa. As a conclusion, one is able to think that the proposed non-probabilistic method can play an important role in uncertain quantification of composite laminates, which can lead to a relatively believable and rational result.
## 6 Defenses against Model and Data Attacks
### Robust Client Selection Techniques  
Robust client selection techniques are implemented on the server side to ensure that only the most dependable and trustworthy clients contribute to the FL process. This minimizes the likelihood of incorporating malicious updates and helps to maintain the integrity of the model aggregation process. This process is illustrated in Figure 10.  
Building on this concept, the authors in [110] proposed an ensemble framework to defend against malicious clients in FL. By training multiple global models using randomly sampled subsets of clients, this approach mitigates the impact of malicious updates. During inference, the ensemble model aggregates predictions via majority voting, providing resilience against a bounded number of malicious clients. A \"certified security level\" for each test example is derived based on the gap between the highest and second-highest label probabilities across the models, indicating robustness. This adaptable defense maintains high \"certified accuracy\" against adversarial behaviors, as validated empirically, demonstrating the importance of secure client selection.  
Expanding on the need for diversity and accuracy in client selection, the authors in [111] introduced the DivFL framework. This approach selects a diverse subset of clients through submodular maximization to approximate the full gradient with minimal error. Using a greedy algorithm, DivFL efficiently maximizes a submodular facility location function to select representative clients. Integrated with FedAvg, DivFL substitutes averaging over all the clients by aggregating updates only from the selected diverse clients after they perform local SGD updates on their data. The framework's convergence analysis shows that it achieves an \(O(1/T)\) convergence rate similar to FedAvg, with an additional error term dependent on the gradient quality. This ensures effective performance, particularly in scenarios with non-IID and heterogeneous client data distributions, emphasizing the value of strategic client selection for robust FL.  
Complementing these techniques, FedClean offers a robust defense framework against parameter poisoning attacks in FL [112]. It employs a reputation-based agent sampling strategy using Bayesian principles to assign reputation scores, prioritizing agents with higher reputations for contributing to the global model update. Additionally, FedClean features a peer truth-serum-aided hypersphere-based update selection mechanism that utilizes  
Figure 10: Visual representation of robust client selection.  
a hypersphere classifier and an oracle for distinguishing between honest and malicious updates. This approach, combined with reputation-based weighted averaging aggregation and an oracle-driven detection framework, enhances security without compromising model convergence and performance stability, comparable to FedAvg.
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 4 Methods to Reduce Energy Losses
### Technical Energy Losses
A notable upward trend has been evident since 1990, the beginning of the decade, with visible results, and this increase persists until 2013, the beginning of the second decade of reference, when there is a marked increase in interest in this field. Presently, the trend continues to rise, with a multitude of studies being conducted annually, showcasing methods to minimize these energy losses. As illustrated in Figure 11, the leading countries in terms of scientific material production concerning the reduction of technical energy losses have been determined. The data presented here are derived from the Scopus database and reflect the ranking. The United States is the leading contributor of scientific materials, followed by China. Germany and the United Kingdom have also demonstrated interest in the subject of reducing energy losses generated by technical elements. France, Brazil, and Canada are at the bottom of the ranking, but they are maintaining a high trend. Figure 10: Graph on the evolution of the number of publications on technical loss mitigation.
# mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
## 1 Introduction
In addition, in order to obtain as much information as possible, it is often necessary to project multiple targets that are in neighboring different distance planes onto one distance plane, which is not available in conventional imaging algorithms and requires more processing steps, so a one-step projection method can greatly reduce the workload. The problem of projecting multiple targets at different distances planes to the same distance plane was solved in the literature [16; 17] using the Maximum Projection method, but it requires a combination of multiple results to achieve this, which cannot be achieved in one step. In summary, there are still many challenges in the field of millimeter-wave SAR imaging, especially the ghost image problem of SISO Sparsely-Sampled and the projection problem of multiple targets at different distances. Therefore, this paper addresses these challenges and does related research, and the contributions made are as follows. * A simple millimeter-wave near-field SAR imaging system is developed with an easy-to-build \"T\" structure for the rail part, and the complexity of the radar part is reduced by using the SISO, monostatic approach. * A robust millimeter-wave SAR imaging method is proposed, including three parts: windowing, imaging, and noise reduction. The Blackman Window used aims at the projection problem of multiple targets at different distances, the Analytic Fourier Transform imaging algorithm used aims at ghost image under SISO Sparsely-Sampled, and the Mean Filter used aims at spatial noise. * We have conducted a large number of imaging experiments, and the results show that the mmSight can image a single target with Fully-Sampled, a hidden target, and multiple targets at the same distance, in addition to solving the ghost image problem under SISO Sparsely-Sampled conditions and the projection problem of multiple targets at different distances, which indicates that the mmSight enhances the robustness of the imaging system. This paper consists of five sections. Section 2 introduces the work related to millimeter wave imaging; Section 3 details the proposed imaging method, which mainly contains two parts: data acquisition and target imaging; Section 4 shows the experimental platform, experimental results, and also evaluates the experimental results; Section 5 concludes the paper.
## 3 Architecture of the Proposed Solution
Each VM on the compute node behaves as an individual process and is assigned a unique process ID (PID). To access the current status of and information on the VMs, our monitoring solution utilizes the /procfs folder on the compute node. For the data collection necessary to monitor the CPU steal time of the VMs, our proposed solution incorporates specific algorithms, namely Algorithm 1 and Algorithm 2.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 3 Signal Measurement Techniques
### Connectivity  
Received Signal Strength (RSS)  
This method is based on the received power of a signal, measured in dBm, and on the relationship between the attenuation of the signal and the distance traveled (Figure 7). Knowing the power with which the signal is emitted and, of course, the power with which the signal arrives at the receiver, it is possible to calculate its attenuation. In fact, since the attenuation of the signal is directly proportional to the distance, with the use of theoretical and empirical models based on the law of signal propagation, it is possible to derive the distance once the attenuation is known [29]. Devices using this technique have the advantage of being low power consumption essential features in the case of WSN (wireless sensor network) nodes, which typically have limited power.  
The Friis formula can be used to evaluate the signal strength received:  
\[P_{R}=P_{T}\frac{G_{T}G_{R}\lambda^{2}}{\left(4\pi\right)^{2}d^{n}}, \tag{6}\]  
where \(P_{R}\) is the received signal power, \(P_{T}\) is the transmitted signal power, \(G_{R}\) is the gain of the receiving antenna, \(G_{T}\) is the transmitting antenna gain, \(\lambda\) is the wavelength (\(\lambda=c/f\), where \(c\) is the speed of wave propagation, and \(f\) is its frequency), \(d\) the distance in meters, and \(n\) the signal propagation constant, also called propagation exponent, dependent on the environment in which we are located.  
Considering that the power of the transmitted signal decays with distance, we can modify the previous relationship with the log-normal shadowing model used in wireless communications [30]:  
\[P_{r}(d)=P_{0}-10n\log d/d_{0}+\zeta_{\sigma}, \tag{7}\]  
where \(P_{r}(d)\) is the received power at distance \(d\), \(P_{0}\) is the received power measured at reference distance \(d_{0}\), \(n\) is the path-loss exponent, and \(\zeta_{\sigma}\) is the zero-mean Gaussian noise.  
Table 2 compares the different signal measurement techniques through their main characteristics.  
Figure 6: Angle of arrival.  
Figure 7: Received signal strength.
## 4 Experimental Setup
### Vector Network Analyzer  
The parasitic inductance is mainly determined by the devices package. However, the above approach suffers from being time-consuming and having poor convergence, since the physical structure of a package is complex. There is a more practical approach which is based on Impedance Analyzer or Vector Network Analyzer (VNA) [32]. By calibrating the test system using a mathematical technique called vector error correction, VNA provides high measurement accuracy [33]. The power device is performed over a frequency range of 100 kHz to 200 MHz by Keysight VNA (E5061B). Since the MOSFET under zero applied voltage is equivalent to a second-order RLC circuit, as shown in Figure 7, it has a self-resonant frequency (SRF). The parasitic inductances and internal resistances can be calculated through its SRF [34]. The testing temperature also ranges from 25 to 150 \({}^{\circ}\)C.
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
## 5 Conclusions  
In this paper, we described an effective query-by-example retrieval system that can be used for the exploitation of Earth Observation SITS. The strategy is based on a two-step procedure. The first step consists of measuring the degree of similarity between a query provided by the user and other spatio-temporal evolutions in SITS. The second step consists of separating similar and non-similar  
Figure 15: Analysis of long Landsat SITS: (**a**) DTW-based K-means clustering [7], (**b**) LDA-based clustering [12].  
patterns via an optimal thresholding technique applied over the DTW distance image obtained in the first step.  
The experiments showed that, even if the SITS is irregularly sampled, affected by clouds or haze, or if the acquisitions are performed in different seasons and under different illumination conditions, the method is able to recognize specific patterns in SITS. In order to show the effectiveness and applicability of our proposed retrieval method, we considered several application scenarios. First, we considered the problem of retrieving similar spatio-temporal patterns from the SITS by analyzing a specific case, namely the construction of two accumulation lakes with different histories. In the second scenario, we exploited the proposed method to assess the damage that floods produce over a region. This is a typical scenario in emergency situations when a fast and effective evaluation of the affected areas is mandatory. In the last scenario, we considered long SITS and aimed to build land cover and land use maps that provide fundamental information for many applications, including change analysis, crop estimation, sustainable management of natural resources, and urbanization planning.  
Being characterized by a low computational complexity, the proposed method for retrieving similar spatio-temporal evolutions based on a specific query represents a good candidate for the online analysis and annotation of SITS. Moreover, the method is designed to use the entire information provided by a multispectral remote sensing sensor, regardless of the number of spectral bands.  
The contribution to this work is as follows: conceptualization, A.R. and C.B.; methodology, A.R.; software, A.R.; validation, A.R.; writing--review and editing, A.R.; review and supervision, C.B.; funding acquisition, C.B.  
This work has been funded by University Politehnica of Bucharest, through the Excellence Research Grants Program, UPB GEX 2017. Identifier: UPBGEX2017, Ctr. No. 32/2017.  
The authors thank NASA USGS for making freely available the datasets for research purposes.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
SITS Satellite Image Time Series USGS United States Geological Survey DTW Dynamic Time Warping LDA Latent Dirichlet Allocation SVM Support Vector Machine ML Maximum Likelihood MLE Maximum Likelihood Estimation EM Expectation-Maximization OA Overall Accuracy MAR Missed Alarm Rate F False Alarm Rate
# Generating Super Spatial Resolution Products from Sentinel-2 Satellite Images +
###### Abstract  
Access to high spatial resolution satellite images enables more accurate and detailed analysis of these images. Furthermore, it facilitates easier decision-making on a wide range of issues. Nevertheless, there are commercial satellites such as Worldview that have provided a spatial resolution of fewer than 2.0 m, but using them for large areas or multi-temporal analysis of an area brings huge costs. Thus, to tackle these limitations and access free satellite images with a higher spatial resolution, there are challenges that are known as single-image super-resolution (SISR). The Sentinel-2 satellites were launched by the European Space Agency (ESA) to monitor the Earth, which has enabled access to free multi-spectral images, five-day time coverage, and global spatial coverage to be among the achievements of this launch. Also, it led to the creation of a new flow in the field of space businesses. These satellites have provided bands with various spatial resolutions, and the Red, Green, Blue, and NIR bands have the highest spatial resolution by 10 m. In this study, therefore, to recover high-frequency details, increase the spatial resolution, and cut down costs, Sentinel-2 images have been considered. Additionally, a model based on Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) has been introduced to increase the resolution of 10 m RGB bands to 2.5 m. In the proposed model, several spatial features were extracted to prevent pixelation in the super-resolved image and were utilized in the model computations. Also, since there is no way to obtain higher-resolution (HR) images in the conditions of the Sentinel-2 acquisition image, we preferred to simulate data instead, using a sensor with a higher spatial resolution that is similar in spectral bands to Sentinel-2 as a reference and HR image. Hence, Sentinel-Worldview image pairs were prepared, and the network was trained. Finally, the evaluation of the results obtained showed that while maintaining the visual appearance, it was able to maintain some spectral features of the image as well. The average Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Spectral Angle Mapper (SAM) metrics of the proposed model from the test dataset were 37.23 dB, 0.92, and 0.10 radians, respectively.  
high spatial resolution; super-resolution; ESRGAN; PSNR; SAM; Sentinel-2 +
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