esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of InSAR Methodology
### Multi-Pass InSAR Techniques for the Retrieval of Surface Displacement Time-Series
The analysis resulted in a very significant density of SAR measure points (in average, about 15,000 points/km\({}^{2}\)), showing the improved capability of the high spatial resolution SAR sensors in detecting and assessing ground deformations induced by active landslides in urban and sub-urban areas [24].
## 3 Dynamic Model of the Horizontal System
Substitute Equations (2) and (5) into Equation (14), and, after rearrangement, the horizontal system analysis model of the MAR system can be obtained:  
\[ \begin{bmatrix}0.21&0.21\\ -0.0268&0.0268\end{bmatrix} \cdot \begin{bmatrix}i_{cl}\\ i_{cl}\end{bmatrix} = \begin{bmatrix}1.83&0\\ 0&0.0273\end{bmatrix}\begin{bmatrix}\frac{\partial^{2}y_{br}}{\partial\xi^{2} }\\ \frac{\partial^{2}y_{b}}{\partial\xi^{2}}\end{bmatrix}  \tag{18}\]  
The boundary conditions for Equation (18) are defined by the physical constraints of the maglev ruler:  
Initial states: \(y_{b}(0)=0\), \(\psi_{z}(0)=0\);  
Motion range limits: \(y_{b}\leq 1.2\) mm, \(\psi_{z}\leq 0.01\) rad to avoid mechanical interference;  
Actuator saturation: \(i_{cr}\leq 2\) A, \(i_{cl}\leq 2\) A. These constraints reflect the system's nonlinearity and coupling, which cannot be handled by linear controllers like PID. It can be seen from Equation (18) that the horizontal system of the MAR system is a nonlinear system with a double-input and double-output structure. Moreover, there is a strong coupling in the system. Conventional linear control methods cannot effectively control the system. The ADRC method is more suitable for the horizontal system of the magnetic levitation ruler system.
###### Abstract  
Detecting fraudulent behaviors in electricity consumption is a significant challenge for electric utility companies due to the lack of information and the complexity of both constructing patterns and distinguishing between regular and fraudulent consumers. This study proposes a methodology based on data analytics that, through the processing of information, generates lists of suspicious metering systems for fraud. The database provided by the electrical distribution company contains 266,298 records, of which 15,013 have observations for possible frauds. One of the challenges lies in managing the different variables in the training data and choosing appropriate evaluation metrics. To address this, a balanced database of 27,374 records was used, with an equitable division between fraud and non-fraud cases. The features used in the identification and construction of patterns for non-technical losses were crucial, although additional techniques could be applied to determine the most relevant variables. Following the process, several popular classification models were trained. Hyperparameter optimization was performed by using grid search, and the models were validated by using cross-validation techniques, finding that the ensemble methods Categorical Boosting (CGB), Light Gradient Boosting Machine (LGB) and Extreme Gradient Boosting (EGB) are the most suitable for identifying losses, achieving high performance and reasonable computational cost. The best performance was compared by measuring accuracy (Acc) and F1 score, which allows for the evaluation of various techniques and is a combination of two metrics: detection rate and precision. Although CGB achieved the best performance in terms of accuracy (Acc = 0.897) and F1 (0.894), it was slower than LGB, so it is considered the ideal classifier for the data provided by the electrical distribution company. This research study highlights the importance of the techniques used for fraud detection in electricity metering systems, although the results may vary depending on the characteristics of the training, the number of variables, and the available hardware resources.  
on-technical electrical losses; data analytics; utility; energy measurement; machine learning 2020
## 1 Introduction  
The invention of high efficiency white Light Emitting Diodes (LEDs) has created a new chapter for the use of energy and lighting [1; 2; 3]. As the white LEDs have been widely used in interior lighting, the fact that short wavelength light causes damage to eyes due to the wave crest in the blue band of the LED spectrum, especially the cheap ones, has drawn great attention [4; 5; 6; 7]. The common resolution of the wave crest in the blue band of the LED spectrum is through the control of the red, green and blue (RGB) or the use of fluorescent substances to absorb the excessive blue light [8; 9; 10; 11]. However, the former way needs expensive electronic devices to achieve the matching of each component of the RGB; the latter method has the output of the white LED fixed, which limits their application ranges.  
Recently, the enhanced selected light transmission in the visible spectrum of metal nanostructures such as Ag, Au, Al, and Cu has been designed and widely used in the antireflection layer of solar cells, the transmission-increasing layer in LEDs, and even in the highest possible resolution color filters [12; 13; 14; 15]. Furthermore, a cheap way of fabricating large-scale quasi-ordered hexagonal close-packed Al nanodents on Al foil using electrochemical etching has been reported [16; 17]. These Al nanodents show optical characteristics similar to those of sub-wavelength grating [18], exhibiting strong near-field enhancement and selected light scattering properties. As a result, the application of Al nanodents has become a new choice for light filters in LED sources.  
Herein, quasi-ordered hexagonal close-packed Al nanodents were prepared and used to manage the output spectrum of white LEDs. The selected scattering enhancement of short wavelength light with a peak of 450 nm was found on Al nanodents, with the increment of the incidence angle ranging from 30\({}^{\circ}\) to 70\({}^{\circ}\). However, long wavelength light with a peak of 550 nm was found to show weaker scattering under the same conditions. Meanwhile, slight peak shifts in the output of long wavelength light were observed. The reason why Al nanodents exhibit selected scattering characteristics was clarified by finite difference time domain (FDTD) simulations.
## 4 Discussion and Outlook
### Possible Improvement of InSAR-Based Detection and Monitoring  
The C-band Sentinel-1 mission currently provides free, open-access, medium-resolution SAR image data with worldwide coverage, which we used in this study for various geotechnical engineering related phenomena. The datasets were processed using SNAP and StaMPS which are also open-source software packages. The potential of using shared data and tools can be further utilized for recurring geohazards caused by natural phenomena and human activities around the world. This is possible by (1) combining both Sentinel-1A/B SAR data to achieve an effective 6-day revisiting period which is shorter compared to the 12-day repeat cycle for one constellation alone (if available), (2) combining both ascending and descending data from the same orbit to obtain the true N-S and E-W ground motions, (3) acquiring short-time span and high-resolution SAR products (i.e., from COSMO-SkyMed and TerraSAR-X), and (4) using SAR data acquired with longer radar wavelength (i.e., from ALOSPALSAR 2). Furthermore, (5) no means least is the integration of InSAR techniques with other surveying methods. Use of precise leveling for monitoring of ground movements can complement the satellite-based remote sensing techniques after InSAR detects a potential threat due to ground deformations caused by natural factors and human activities. These further improvements ensure more efficient temporal decorrelation consideration and more PS detection capability even in sub-urban and/or rural areas. As one of the possible examples, these enhancements will allow to inspect and manage the tunnel construction sequence before proceeding to the next stage when untoward incidents happen. Infrastructures and people's lives can also be safeguarded against landslides if an early detection method of precursory slope movement is in place.
## 3 Parametric Model Definition
### Surface Meshing Method
#### 3.1.5 Surface Extension and Trimming  
As seen before, the laths of the grid are curves interpolated from the points obtained from the ACM. To make the laths reach the surface boundaries, it is necessary for the ACM to continue beyond those boundaries for at least one iteration over the extended surface, so the last points for the interpolation are outside the boundaries allowing the interpolated curves to intersect with them. Therefore, it is necessary to implement an intermediate step in the process before generating the laths from the grid of points: the surface must be extended as necessary, with continuity of tangency and curvature, to get points outside the surface and make the laths intersect the perimeter [34].  
In Figure 14, the ACM is performed over an extended version of the surface. The mesh is then generated by interpolating the curves at these points (Figure 14a). The points are removed, and the laths are trimmed at the surface boundary (Figure 14b). This extension process may be large when dealing with surfaces with noticeably uneven length and width dimensions to avoid unreachable blind zones for the spheres to intersect.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy Improvement (mm)**} \\  & **Test Point O\({}_{1}\)** & **Test Point O\({}_{3}\)** & **Multiplier of** \\
**Method** & \(r_{\rho,\text{mean}}\)1  = 205.71 m** & \(r_{\rho,\text{mean}}\)1  = 17.89 m & **Runtime** \\  & **Path Length = 15.33 m** & **Path Length = 17.22 m** & \\ \hline ICM & 4.48 & 8.46 & \(\times\)6.77 \\ ACM & 4.47 & 8.42 & \(\times\)2.66 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of accuracy improvement and runtime of the Improved Compass Method (ICM) and the Adaptative Compass Method (ACM) over the Compass Method (CM).  
Figure 14: (**a**) Extended mesh and laths over the extended surface; (**b**) Trimmed mesh result over the test surface.  
Figure 13: Test surface and curvature radius diagrams of the paths to the test points \(O_{1}\) and \(O_{3}\).
# High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
###### Abstract  
Existing diffusion models outperform generative models like Generative Adversarial Networks in image synthesis and editing. However, they struggle with high-precision image editing while preserving image details and the accuracy of editing instructions. To address these challenges, we propose a dual attention control method to achieve high-precision image editing. Our approach includes two key attention control modules: (1) cross-attention control module, which combines the cross-attention maps of the original and edited images through weighted parameters, ensures that the synthesized edited image retains the structure of the input image. (2) Self-attention control module, which varies based on the editing task, applied at \"coarse\" and \"fine\" layers, since the coarse layers help maintain input image details and the fine layers are better suited for style transformations. Experimental evaluations have demonstrated that our approach achieves excellent results in detail preservation, content consistency, visual realism, and semantic understanding, making it especially suitable for tasks requiring high-precision editing. Specifically, compared to the editing outcomes under no control conditions, the introduction of dual visual attention control has led to an increase of 6.19% in CLIP scores, a reduction of 29.3% in LPIPS, and a decrease of 24.7% in FID. These significant improvements not only validate the effectiveness of the dual attention control but also attest to the method's substantial flexibility and adaptability across different scenarios. Notably, our approach is a zero-shot solution, requiring no user optimization or fine-tuning, facilitating real-world applications.  
image editing; diffusion model; image synthesis; attention control
## 4 Discussion and Conclusions  
In this work, we propose a joint algorithm for MFI and OSNR estimation based on the DBSCAN clustering method. The cluster number and the proportion of core points are extracted by the DBSCAN algorithm to achieve MFI and OSNR estimation. The numerical simulation results show that for five widely used signal types (PDM-QPSK, PDM-8 PSK, PDM-16 QAM, PDM-32 QAM, and PDM-64 QAM), the MFI can achieve 100% accuracy when the incoming OSNR values are higher than their respective 7% FEC thresholds. The mean estimated error is 0.3 dB, 0.2 dB, 0.3 dB, 0.5 dB, and 0.6 dB for five MFs, respectively. The experimental results demonstrate that, with the OSNR range not lower than 7% threshold of QPSK and 16 QAM signals, the MFI decision threshold proposed in Section 2.2 can correctly divide two MF signals. In addition, the mean estimated error for QPSK signal is 0.4 dB (back to back system) and 0.4 dB (100 km fiber transmission system) and for 16 QAM is 0.5 dB (back to back system) and 0.5 dB (100 km fiber transmission system). The experimental results prove that the proposed method is feasible in practical optical fiber communication systems. There is no additional hardware required, so the proposed method has the ability to be integrated into existing optical performance monitoring systems without burden. Furthermore, the proposed method has the potential to be used in BER calculation, and linear or nonlinear impairments monitoring. We believe that our multifunctional and simple method would be more favorable in comparison to future EON.  
Conceptualization, Z.Z. and A.Y.; methodology, Z.Z. and A.Y.; software, Z.Z.; validation, Z.Z., and A.Y.; formal analysis, Z.Z. and P.G.; investigation, Z.Z.; resources, Z.Z. and Q.T.; data curation, Z.Z. and Q.T.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z., A.Y. and P.G.; visualization, Z.Z.; supervision, A.Y. and P.G.; project administration, A.Y.; funding acquisition, A.Y. All authors have read and agree to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61427813 and Open Fund of IPOC (BUPT), grant number IPOC2018B003.  
This work is supported by State Key Laboratory of Advanced Optical Communication Systems and Networks, China and Key Laboratory of Photonics Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, China.  
The authors declare no conflict of interest.
## 4 Discussion
### Clinical Implications  
Clinical implications of conducting a study on the relationship between heart rate variability (HRV) and position include the following:  
Improved Diagnosis and Risk Assessment: Conducting such a study can enhance our understanding of how different body positions affect HRV and autonomic function. This knowledge can aid healthcare professionals in diagnosing and assessing the risk of cardiovascular disorders more accurately. By incorporating position-specific HRV measurements into clinical evaluations, clinicians can obtain valuable information about an individual's autonomic health and potential cardiovascular risks.  
Tailored Interventions: The findings of this study can guide the development of targeted interventions aimed at optimizing HRV and autonomic balance. Healthcare practitioners can use this knowledge to prescribe specific postural modifications or exercises that positively influence HRV.  
Rehabilitation and Physical Therapy: Understanding the impact of different positions on HRV can be especially relevant in rehabilitation and physical therapy settings. Therapists can design customized treatment plans that involve specific postures and movements to enhance autonomic function and cardiac health. While using HRV measurements during therapy sessions, we noted that progress can be tracked, and adjustments can be made to optimize the rehabilitation outcomes.  
Workplace Ergonomics: Work-related postures play a significant role in sedentary individuals' health, as they spend a considerable amount of time in specific positions. Conducting a study on HRV and position can inform the development of ergonomic guidelines for various occupations, helping employers create healthier work environments. Recommendations for optimal postural positions and regular movement breaks can be implemented to mitigate the negative impact of prolonged sitting and improve autonomic function among employees.  
Lifestyle Modifications: Findings from the study can motivate individuals to make lifestyle modifications that promote better HRV and autonomic function. Educating the general population about the relationship between posture and HRV can encourage people to adopt healthier postural habits in daily activities and reduce sedentary behaviors. Public health campaigns and educational initiatives can raise awareness and promote the importance of maintaining proper posture for long-term cardiovascular health.
Matrix effects appear as either X-ray absorption or enhancement phenomena, which can affect the determination of a single analyte. In the absence of a clear bias with any possibility to correct the output (e.g., adjusting the calibration slope of the linear regression), those errors may not be corrected. However, those effects can occur particularly in sites characterised by a uncommon geochemical composition, such as metal-bearing mineralised rocks or geological materials affected by mining, where trace elements that normally occur in the environment in the ppm or ppb range can reach notably higher concentrations, up to wt.%, with a variability of several orders of magnitude [12]. Mining exploration and industrial use of common and uncommon elements (e.g., precious metals, rare-earth elements--REEs) have increased in recent years due to an exponentially growing demand. As a result, there is still substantial concern regarding the subsequent risk of dispersion into the environment of those elements known for their high toxicity, such as mercury (Hg) or antimony (Sb). In the last decades, many studies [7; 11; 13; 14; 15; 16; 17] have reported the performance of pXRF instruments based on QA/QC validation procedures using certified reference materials (CRMs), blank samples, and/or traditional laboratory analyses on site-specific samples, studying different groups of elements in various matrices. Moreover, pXRF analyses of soil, lato sensu, samples are usually performed on common elements such as base metal(loid)s (e.g., As, Cu, Fe, Mn, Pb, Zn), whereas, curiously, highly toxic elements such as Hg and Sb have not been considered or have not been found in concentrations high enough and were detected in a small number of studies, e.g., [18; 19]. Thus, the main purpose of this research was to evaluate the advantages and disadvantages of the use of pXRF on samples collected from two different decommissioned mining areas in Italy characterised by Pb-Zn and (Hg-rich) Cu-Sb ore deposits found to have very low to very high concentrations of As, Cu, Hg, Pb, Sb, and Zn. In order to reach this objective, a validation with traditional laboratory analyses considering various metal(loid)s was performed on a large geochemical dataset, including samples of soils, stream sediments, and mining wastes. Moreover, this research may provide insights as to how analytical determinations using pXRF can be influenced by (1) different spectral interferences and matrix effects at Pb-Zn and Cu-Sb mining sites, (2) low concentrations, and (3) heterogeneity and sample preparation.
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Masked Face Recognition Approaches
#### 5.3.2 Masked Region Discarding
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Year** & **Dataset** & **Accuracy** & **mAP** & **Rank-1** \\ \hline \multirow{4}{*}{CA-MRR [94]} & \multirow{2}{*}{2021} & Masked-Webface (Case 1) & 91.525 & - & - \\ \cline{2-5}  & & Masked-Webface (Case 2) & 86.853\% & - & - \\ \cline{2-5}  & & Masked-Webface (Case 3) & 81.421\% & - & - \\ \cline{2-5}  & & Masked-Webface (Case 4) & 92.612\% & - & - \\ \hline \multirow{2}{*}{Hariri [95]} & \multirow{2}{*}{2022} & RMFRD & 91.3\% & - & - \\ \cline{2-5}  & & SMFRD & 88.9\% & - & - \\ \hline UNMaskedArea-MFR (Cosine Similarity) [96] & \multirow{2}{*}{2022} & Custom (Indonesian people) Dataset & 98.88\% & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._an innovative training methodology tailored for MFR, leveraging partial face data to achieve heightened accuracy. The authors curated their dataset, consisting of videos capturing faces across a range of devices and backgrounds, featuring 125 subjects. Drawing from established methodologies, such as employing YOLOv4 [101] for face detection, leveraging the pre-trained VGGFace model for feature extraction, and employing artificial neural networks for classification, the proposed system exhibits significant performance enhancements. While conventional training methods yielded a test accuracy of 79.58%, the adoption of the proposed approach resulted in a notable improvement, achieving an impressive test accuracy of 99.53%. This substantial performance boost underscores the effectiveness of employing a tailored training strategy for tasks related to masked face recognition. [PERSON] et al. [22] curated two datasets specifically tailored for MFR: MFV, containing 400 pairs of 200 identities for verification, and MFI, comprising 4916 images representing 669 identities for identification. These datasets were meticulously developed to address the scarcity of available data and serve as robust benchmarks for evaluating MFR algorithms. To augment the training data and overcome dataset limitations, they introduced a sophisticated data augmentation technique capable of automatically generating synthetic masked face images from existing facial datasets. Additionally, the authors proposed a pioneering approach featuring a two-branch CNN architecture. In this architecture, the global branch focused on discriminative global feature learning, while the partial branch was dedicated to latent part detection and discriminative partial feature learning. Leveraging the detected latent part, the model extracted discriminative features crucial for accurate recognition. Training the model involved utilizing both the original and synthetic training data, where images from both datasets were fed into the two-branch CNN network. Importantly, the parameters of the CNN in the two branches were shared, facilitating efficient feature learning and extraction.