esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Experimental Procedure
### Microstructural and Surface Roughness Evaluation  
After conducting E-C experiments, the surface of copper samples was examined. Optical microscopy (Model Olympus, Olympus Corporation, Tokyo, Japan) \"for annealed\" and scanning electron microscope (SEM, JEOL-JSM 5510, JEOL, Ltd., Tokyo, Japan) \"for ECAPed samples\" were employed. The surface roughness of samples was evaluated by using an optical profiling system, manufactured by (Contour GT-K1, Bruker, Billerica, MA, USA). The profiling system works by a technique of streamlined interface and intuitive work flow. It utilizes white and green light interferometry. The profiling system can perform fast three-dimensional surface measurements from millimeter-scale, to nanometer scale with sub-nanometer resolution. The combination of the easy measurement setup, fast data acquisition, and small footprint allow the Contour GT-K1 to deliver 3D surface metrology performance. Furthermore, the roughness parameters of eroded surface at different ECAP passes were studied through measuring the parameters \(R_{a}\), \(R_{t}\), and \(R_{q}\). The arithmetic average of the absolute value (\(R_{a}\)) was recorded and calculated by using the equation:  
\[R_{a}=\ \left(\
icefrac{{1}}{{L}}\right)\int\limits_{0}^{L}Z(x)dx \tag{5}\]  
where \(L\) is the evaluation length and \(Z\) (\(x\)) is the profile height function. The maximum profile height \(R_{t}\) of copper through four passes of ECAP was also measured. Furthermore, the root mean square (\(R_{q}\)) which is the average between height deviations and mean line/surface, taken over the evaluation area. The roughness parameter \(R_{q}\) represents the standard deviation of the profile heights. The equations to calculate \(R_{q}\) in both two and three dimensions are as follows [39]:  
\[R_{q}=\ \sqrt{\frac{1}{n}\ \sum\limits_{i=1}^{n}\left(Z_{i}-\overline{Z} \right)^{2}} \tag{6}\]  
\[R_{q}=\ \sqrt{\frac{1}{MN}\ \sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}Z^{2}\ \left(x_{i}-y_{j}\right)} \tag{7}\]  
where \(M\) and \(N\) are the data points in \(X\), \(Y\), and \(Z\) directions relative to the mean plane.
## 5 Experimental Section  
### Performance Analysis Experiments  
The experimental part of the performance analysis for the WRF includes a hybrid parallel analysis of MPI + OpenMP, scalable performance analysis, and hotspot function analysis.  
(a). For the MPI + OpenMP parallel programming experiments, our main objective is to optimise the process * thread combination with the best performance for a given use of resources. In this paper, we use 8, 16, 32, 64, and 128 to run WRF. Specifically, when running WRF with 8 cores, we record the WRF runtime (simulated for 6 h) for each combination of 8 processes * 1 thread, 4 processes * 2 threads, and 2 processes * 4 threads, respectively, for side-by-side comparison, and select the combination with the best performance. The same is repeated for 16, 32, 64, and 128 cores. See Table 1 for details.  
(b). For numerical experiments exploring the scalability of WRF on the Kunpeng 920 platform, we run WRF with 64, 128, 256, and 512 cores using case A. To avoid the impact of IO on the scalability performance, we count the single iteration step runtime in the performance statistics, and since the single iteration step runtime of WRF is relatively stable, we average the runtime of the last ten steps of the simulation over one hour (this is how the later experiments on single time iteration steps are chosen). See Table 2 for details.  
(c). To analyse the hotspot function of WRF, we use Case B to run WRF with 30 cores and use Perf record to record the performance of WRF runtime in perf.data, while monitoring it in real time via Perf top. Finally, the data in perf.data were analysed as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Threads** & **8** & **16** & **32** & **64** & **128** \\ \hline
1 & 2442.55 & 1436.62 & 1000.40 & 563.53 & 310.9 \\
2 & 2466.07 & 1404.12 & 989.75 & 510.37 & 294.41 \\
4 & 2357.44 & 1428.20 & 947.21 & 508.35 & 284.83 \\
8 & 2400.20 & 1426.14 & 958.42 & 493.82 & 278.64 \\
16 & & 1392.33 & 935.92 & 498.37 & 285.23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MPI + OpenMP hybrid parallel preference experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grid Size** & **Process** & **Run-Time/s** & **Efficiency** \\ \hline
1024 \(\times\) 1024 \(\times\) 33 & 64 & 4.49 & 1 \\
1024 \(\times\) 1024 \(\times\) 33 & 128 & 2.32 & 0.968 \\
1024 \(\times\) 1024 \(\times\) 33 & 256 & 1.28 & 0.877 \\
1024 \(\times\) 1024 \(\times\) 33 & 512 & 0.66 & 0.850 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical experiments evaluating the strong scalability performance of WRF on the Kunpeng 920 processor platform.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hotfunction** & **Rate** \\ \hline pow\_finite & 14.13\% \\ \_iface\_progress & 5.36\% \\ \_worker\_progress & 4.34\% \\ advect\_scalar\_pd & 4.11\% \\ opal\_progress & 3.87\% \\ \_advance\_w & 3.78\% \\ \_iface\_progress & 3.45\% \\ \_spcvmc\_sw & 2.81\% \\ \_advect\_scalar & 2.57\% \\ other & 55.58\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hotspot function distribution of WRF.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
###### Abstract  
Free-space optical (FSO) communications have emerged as a promising complement to conventional radio-frequency (RF) systems due to their high bandwidth, low interference, and license-free spectrum. Visible-light FSO communication, using laser diodes or LEDs, offers potential for short-range data links, but performance is highly wavelength-dependent under varying atmospheric conditions. This study presents an experimental evaluation of three visible laser diodes at 650 nm (red), 532 nm (green), and 405 nm (violet), focusing on their optical output power, quantum efficiency, and modulation behavior across a range of driving currents and frequencies. A custom laboratory testbed was developed using an Atmega328p microcontroller and a Visual Basic control interface, allowing precise control of current and modulation frequency. A silicon photovoltaic cell was employed as the optical receiver and energy harvester. The results demonstrate that the 650 nm red laser consistently delivers the highest quantum efficiency and optical output, with stable performance across electrical and modulation parameters. These findings support the selection of 650 nm as the most energy-efficient and versatile wavelength for short-range, cost-effective visible-light FSO communication. This work provides experimentally grounded insights to guide wavelength selection in the development of energy-efficient optical wireless systems.  
free-space optical communication (FSO); laser diodes; visible-light communication (VLC); quantum efficiency; modulation; atmospheric attenuation; wavelength optimization 2025
# Independent Double-Boost Interleaved Converter with Three-Level Output
## 1 Introduction
The present work introduces a novel interleaved converter topology that integrates two classic boost converters working independently, in parallel at the input and in series at the output. The circuit has high voltage gain capabilities, and in comparison with the work in [14; 15], it exhibits pure capacitive filtering at the output, which enables a classical PI voltage control method to be used. Additionally, with three voltage level characteristics reached at the output in conjunction with the asymmetrical/independent operation of the integrated boost structures, the proposed converter topology could also be used in voltage balancing or interconnection with bipolar DC grids/applications [32]. Another major contribution of the present work is the unique integration of a multitude of useful features, such as interleaved, high-gain, good efficiency, low voltage stress, three-level output with symmetric/asymmetric operation, and stable control method, into a single electronic conversion solution. Apart from the introduction, the paper is organized in the following structure. In Section 2, a detailed representation of the proposed topology and switching states is presented. Furthermore, an analysis of the converter in both the continuous conduction mode and discontinuous conduction mode is discussed in detail. Section 3 presents a study of the converter's behavior, performed by software simulation and practical measurements on a laboratory model. In addition, the main features of the proposed topology in comparison with similar approaches are emphasized in this section. The conclusions and further work are presented in Section 4.
## 3 Energy Absorption Characteristics  
_Evaluation Criteria_  
During the collision process, the anti-climb energy-absorbing device primarily absorbs energy through the plastic crushing deformation of the aluminum honeycomb and the inner and outer thin-walled tubes. In addition to considering the total energy absorbed by the anti-climb device, it is also important to avoid excessively high collision peak forces, as these can lead to high-impact accelerations that may cause injury to passengers inside the vehicle. Therefore, this paper employs the specific energy absorption and the peak crush force to evaluate the energy absorption effectiveness.  
(1) Specific Energy Absorption  
The specific energy absorption (_SEA_) represents the energy that a unit mass of the energy-absorbing structure can absorb. The higher the specific energy absorption, the better the energy absorption effect of the structure. To enhance the crashworthiness of the structure and meet the design requirements specifying the device should be lightweight, the specific energy absorption of the energy-absorbing structure should be maximized.  
\(Q\) represents the energy absorbed by the energy-absorbing structure during its buffering deformation and is defined as follows:  
\[Q=\int_{0}^{l}F(l)dl \tag{1}\]  
In the formula, \(F(l)\) represents the relationship between the impact force and the displacement function, and \(l\) is the effective crushing deformation displacement.  
_SEA_ is an important index of crashworthiness, representing the capacity for energy absorption per unit mass, and it directly reflects the quality of the energy-absorbing structure design. The formula is as follows:  
\[SEA=\frac{Q}{M} \tag{2}\]  
In the formula, \(M\) represents the mass of the anti-climb energy-absorbing device.  
Figure 3: Impact parameters of the anti-climb energy-absorbing device.  
(2) Peak Force  
Peak force \(F\) refers to the maximum impact force during the energy absorption process, which typically occurs in the initial stage. The magnitude of the peak force has a significant impact on the structural safety of the train and the comfort of the passengers, and it is desirable for it to be as close as possible to the mean force. The peak force is an important metric for measuring the energy absorption capacity of an energy-absorbing device during a collision. A smaller peak force implies that the collision energy can be more effectively absorbed and dispersed, thereby reducing the impact force transmitted to the main body of the vehicle and its occupants.  
Mean force \(F_{\text{M}}\) is the average value of the impact force over a specified crushing displacement. Its formula is as follows:  
\[F_{M}=\frac{Q}{l} \tag{3}\]
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 3 Results  
### Evaluation of the Proposed Algorithms on the MRIs from the BrainWeb Dataset  
In our first experiment, we used the simulated MR images in the BrainWeb dataset [43; 45] to evaluate the performance of our registration algorithms. Each image was geometrically distorted using a random perturbation to generate the floating image (Examples in Figure 1). Specifically, a \(14\times 14\) uniform grid was randomly perturbed (by a uniform distribution on \([-6\)\(6]\)) and used as the Free Form Deformation (FFD) grid for the floating image. Then, the distorted images were registered back to the original image using our two proposed algorithms as well as a standard benchmark intensity-based algorithm [22]. For each algorithm, four registration processes were performed, each using one of the SSD, CC, RC, or MI similarity measures. In all of the cases, FFD [44] was used as the model of geometric transform. FFD was implemented via three hierarchical levels of B-spline control points. We set the number of levels in IMFs, \(n\), to 3 in order to have a fair comparison with the intensity-based registration. Iterative Gradient descent was used to optimize the transformation parameters. The accuracy was evaluated using both T-RMSE and I-RMSE.  
We started the experiments by registering images without any bias field noise. Then, we manually added spatially varying distortions or bias field noise to both floating and reference images [22]. The noise was generated using the mixture of Gaussian functions and was added to the images according to Equation (1). The mean of the Gaussian functions was selected at random. The standard deviation was set to \(\frac{1}{16}\) of the image width.
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 1 Introduction  
In recent years, integrating facial recognition systems across diverse sectors, such as security, healthcare, and human-computer interaction, has revolutionized identity verification and access control. Nevertheless, the widespread adoption of face masks in response to the global COVID-19 pandemic has introduced unprecedented challenges to the remarkable performance of conventional facial recognition technologies. The masking of facial features has spurred research initiatives in masked face recognition, prompting the application of innovative deep learning techniques to address this novel challenge. This sets the stage for exploring advanced strategies in face recognition, particularly under challenging conditions involving small or partially obscured faces [1].  
MFR poses a significant challenge in identifying and verifying individuals who wear face masks. This task is complex due to the partial occlusion and variations in appearance caused by facial coverings. Masks obscure critical facial features like the nose, mouth, and chin, and their diverse types, sizes, and colors add to the complexity. It is essential to distinguish between MFR and FMR. While FMR focuses on detecting mask presence, MFR aims to identify and verify individuals wearing masks. Additionally, FU endeavors to remove facial coverings and restore a clear facial representation. Figure 1 visually summarizesour survey's essence, illustrating three distinct tasks--MFR, FMR, and FU--with different outputs for the same input, highlighting the task-specific outcomes.  
Deep learning has emerged as a promising avenue for addressing the challenges of MFR. Algorithms can be trained to discern facial features, even when partially obscured by masks. Proposed MFR methodologies based on deep learning encompass holistic approaches, mask exclude-based approaches, and mask removal-based approaches. Holistic approaches employ deep learning models to discern features of entire faces, leveraging attention modules. Mask exclude-based approaches train models to recognize features of the unmasked facial half, such as the eyes and head. Approaches based on mask removal leverage generative adversarial networks (GANs) to create lifelike facial images from their masked counterparts, as demonstrated in methods [2; 3], facilitating subsequent recognition. The adaptability of deep learning underscores its crucial role as a transformative technology in the realm of MFR. Although deep learning-based MFR methods have demonstrated state-of-the-art performance on various public benchmark datasets, numerous challenges persist that require resolution before widespread deployment in real-world applications becomes feasible.
## 3 Results and Discussion
### Using the Low-Cost Acquisition System to Calculate Acoustic Power  
Figure 11 illustrates the clear and low-noise signal outputs recorded by P1.2 and P2 sensors over a 6 s interval, without any requirement for further smoothing or processing.  
The sensors' performance, as seen in Figure 11, confirms their appropriateness for capturing high-quality acoustic data. The enhancement in data acquisition frequency and the systematic preprocessing of the raw data, as established and described in Section 2.3.3, enabled direct plotting of highly resolved signals. The precise data handling and the ability to maintain signal integrity, especially near the sensors' full scale, are demonstrated, showcasing significant improvements over prior methodologies that struggled with lower amplitude and higher noise levels in different testing setups [15].  
Additionally, the sinusoidal fitting process detailed in Section 2.3.3 was executed to obtain the results of critical wave parameters accurately and automatically for P1.2 and P2 sensors, shown in Figure 12.  
In the merged signals graph of the P1.2 and P2 pressure sensors presented in Figure 12, an interval of 0.16 s displays a phase shift close to 90\({}^{\circ}\) between both signals. This behavior aligns with the theoretical principles of the RC load method, constituting the initial validation point for the system.  
Figure 11: Raw pressure vibration signals measured during the data acquisition after the first treatment in R. The absolute pressure sensor P2 is located in the RC load, P1.2. is located in the resonator, and both of them are linked to the low-cost acquisition system.  
Based on the results, and in accordance with Equation (5), it is possible to calculate the power from the available pressure measurements using the RC load method. This approach enables the determination of the instantaneous amount of acoustic power lost due to dissipation in the load. As a result, the maximum acoustic power obtained on the RC load was \(\dot{E}(load)=112.494\) mW.  
The illustrated results clearly show that the proposed low-cost acquisition system, along with the detailed algorithms, enabled precise and consistent measurements of output acoustic power in the load, thereby highlighting the effectiveness of the data acquisition system in the experimental setups, including those in thermoacoustic devices.
## 5 Critical Challenges of BC Adoption in HMS
Careful clarification is required on the ownership of records, access privileges granted, and distributed storage architecture of blockchain [17,86]. * **Operational Cost Constraints** The costs of establishing and operating a digitized system, as well as the transition from conventional health information systems, are not fixed. It is still not conceived well that an open-source technology and the distributed nature of blockchain can help lower them. The healthcare system based on the blockchain requires constant availability of resources for troubleshooting, upgrading, backup, and reporting purposes. Thus these systems suffer from constraints of operational costs [17,86]. * **Lack of Adoption** In order to provide the necessary computational power for both creating blocks of a transaction and cryptocurrency, blockchain technology needs a network of connected computers. Through incentive systems, participants should be motivated to contribute to computer power. Additionally, it might be necessary to motivate health organizations to use blockchain technology and join the shared network. As more entities participate, the influence of blockchain will grow [17]. * **Transparency** Blockchain technology emphasizes transparency, which may not always be desirable in the healthcare industry. The data replicated on various nodes becomes transparent and could be accessed maliciously by participating nodes. Since healthcare data are critical in nature, sometimes this exposure of sensitive information conflicts with the organization's policies and individuals' interests. Transparency of data on BC is one of the major reasons for the hesitant adoption of BC. Additionally, access to all data pertaining to a user is made possible by hacking or acquiring the user's secret encryption key [17]. * **Selfish Miners Attack** The blockchain is vulnerable to attacks from selfish, complicit miners. Selfish miners hold their mined blocks without broadcasting them, and the public is only made aware of the secret branch if certain conditions are met. All miners would accept the private branch because it is longer than the current public chain. Sincere miners are spending their time on a pointless branch prior to the private BCs release, while selfish miners are mining their own secret chain without interference. Thus, selfish miners typically earn more money. Selfishness might easily reach 51% power as rational miners would be drawn to join it [1].
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 6 Validation and Application of the Proposal
### U-CLX Model Application  
The universities in which the U-CLX Model was applied are virtual distance universities with an excellent track record in virtual higher education at the local and international levels. They have people, processes, procedures, policies, pedagogies, technologies, and the elements for developing U-Learning processes, and they have the necessary U-Learning staff within their institutions to evaluate with the U-CLX Model.  
The U-CLX Model was applied in two universities: Universidad Nacional Abierta y a Distancia--UNAD, a leader in virtual and distance education in Colombia; and Universidad Internacional de la Rioja from Spain, a leader in virtual education in Spain and Latin America. The two universities have developed e-learning processes and work in U-Learning.  
The data were obtained from application of the U-CLX Model using the process designed for institutions. First, the self-evaluation survey was applied to each university's-academic manager, learning engineer, and students. With the data obtained, we measured the level of U-Learning of the institutions by applying the equation defined in the model. Data for each dimension were obtained through the self-evaluation surveys, and these data were used to calculate the level of U-Learning [2].  
The surveys contained questions to enable each person to evaluate the institution according to his/her role. The questions were classified by the components and elements, and the person evaluated each element for the four dimensions: Time, Place, Medium, and Context, taking into account the levels, criteria, and percentages defined in Table 2. When all the respondents had completed the self-evaluation, the model calculated the value of each dimension. With the values of the dimensions, the model's equation was applied to measure ubiquitous learning and define the level of U-Learning in the institution. This was performed in both institutions. The results obtained were compared with the minimum and maximum levels of U-Learning defined in the model in order to assess the institutions' current state and propose improvement plans [2].