esa-sceva/satcom-chunk-collection huggingface dataset:
# RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on Reconstruction
## 4 Experiments
### Encoder and Decoder  
This section aims to validate the effectiveness of dynamic graphs and offset attention. The architecture of Model 1 remains consistent with RE-PU but utilizes static graphs based on point cloud coordinate space in the encoder and employs a self-attention mechanism  
Figure 12: Reconstruction and upsampling.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prior** & **Lattice** & **Fibonacci** & **Hammersley** & **Sphere** & **Lattice** \\
**Distribution** & **Points** & **Lattice** & **Points** & **Uniform** & **Points + Noise** \\ \hline CD \(\downarrow\) & 0.510 & 0.496 & 0.507 & 0.613 & 0.421 \\ \hline \hline \end{tabular} Note: The downward arrows (\(\downarrow\)) indicate that lower values are better for the corresponding metrics.  
\end{table}
Table 3: Quantitative results of different prior distributions.  
in the decoder, serving as the baseline model. Building upon this foundation, Model 2 is derived by replacing static graphs with dynamic graphs, while Model 3 is created by substituting self-attention with offset attention. The complete RE-PU model is obtained by combining dynamic graphs and offset attention. The quantitative results for different models are shown in Table 4. The RE-PU network, incorporating dynamic graphs and offset attention modules, delivers the most promising outcomes, showcasing a considerable enhancement compared to Model 1. Moreover, Model 3 shows a more significant improvement compared to Model 2, implying that offset attention may play a more pivotal role than dynamic graphs. Additionally, it suggests that the decoder might have a greater impact on the overall network performance than the encoder.  
The visualization results in Figure 13 further corroborate that the model incorporating all modules generates point clouds that closely resemble the ground truth. From the bottom of the package in the first row and the shape in the second row, it can be observed that Model 1, Model 2, and Model 3 produce a significant amount of noise and outliers.  
We further explore the impact of the neighborhood size on the dynamic graph. Table 5 illustrates the quantitative comparison across different neighborhood sizes, where bold denotes the best performance. The results demonstrate that the optimal neighborhood size is 20, which aligns with the default setting in our model. Both an overly large or overly small neighborhood can lead to poor results. A neighborhood that is too small will result in insufficient neighborhood information, while a neighborhood that is too large will include more irrelevant information.
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
###### Abstract  
Surface defect inspection based on deep learning has demonstrated outstanding performance in improving detection accuracy and model generalization. However, the small scale of defect datasets always limits the application of deep models in industry. Generative models can obtain realistic samples in a very cheap way, which can effectively solve this problem and thus has received widespread attention in recent years. This paper provides a comprehensive analysis and summary of the current studies of surface defect inspection methods proposed between 2022 and 2024. First, according to the use of generative models, these methods are classified into four categories: Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and multi-models. Second, the research status of surface defect inspection based on generative models in recent years is discussed from four aspects: sample generation, detection objective, inspection task, and learning model. Then, the public datasets and evaluation metrics that are commonly used for surface defect inspection are discussed, and a comparative evaluation of defect inspection methods based on generative models is provided. Finally, this study discusses the existing challenges for the defect inspection methods based on generative models, providing insights for future research.  
deep learning; generative models; surface defect inspection; survey +
Footnote †: journal: Applied sciences
### Duty Cycle
[18] investigated the temporal and spatial variability of Wi-Fi exposure in real-world scenarios (inside a university), demonstrating significant fluctuations in exposure levels depending on environmental factors. To the best of the authors' knowledge, no existing studies have specifically investigated the DC of real-world Wi-Fi 6 network services.
## 3 Results and Discussion
### Mixed Gas Transport Properties  
Mixed gas permeation measurements were carried out on the representative membranes with the highest MOF concentration in each polymer in view of two relevant industrial gas separations, namely biogas upgrading and CO2 capture from flue gas, involving CO2 separation from CH4 and N2, respectively (Figure 8) (See Table SI 4-5). For this purpose, measurements were performed from 1 to 6 bar(a) with simulated flue gas (CO2/N2, 15/85 vol %) and simulated biogas (CO2/CH4, 35/65 vol %). The glassy PEEK-WC MMM exhibits typical dual-mode behavior with the CO2/CH4 mixture, showing a decrease of CO2 permeability as a function of the increased feed pressure, which causes a simultaneous and slightly smaller decrease of CO2/CH4 selectivity. This indicates that the free volume in the PEEK-WC phase and in the MOFs' pores is gradually becoming occupied by CO2, and as a result, the CH4 permeability slightly decreases as a function of pressure. Moreover, some hysteresis of CO2 and CH4 permeability is observed when reducing the pressure after the initial pressure increase steps. This is ascribed to a slight CO2-induced dilation of the polymer, leaving a higher free volume when the pressure is decreased, and thus a higher CH4 permeability and a slightly lower selectivity. In the separation of the CO2/N2 mixture, no further hysteresis takes place because of the lower CO2 partial pressure, and for the same reason the CO2 permeability is slightly higher than in the biogas mixture.  
Contrary to the glassy PEEK-WC, the rubbery Pebax(r)1657/CuNi-MOF (23 wt %) membrane shows essentially pressure-independent permeability and selectivity (Figure 8a), after only a slight increase in permeability with pressure in the first run, apparently due to a certain conditioning of the sample. The transport properties of the Pebax-based membrane are dominated by the rubbery phase, and the dispersed MOFs only provide a higher gas sorption capacity in the membrane but do not significantly affect the overall performance.  
Figure 7: Correlation of the effective diffusion coefficient as a function of the molecular diameter of six light gases in (**a**) Pebax®1657/CuNi MMMs and (**b**) PEEK-WC/CuNi MMMs. Correlation of the effective solubility coefficient of the gases as a function of their critical temperature in (**c**) Pebax®1657/CuNi MMMs and (**d**) PEEK-WC/CuNi MMMs. The legend is identical in all individual graphs.
## 2 Materials and Methods
### Methodology  
In this paper, the ESTARFM was used to fuse GOCI and Landsat data to obtain a Chl-a product with finer spatial and daily temporal resolution simultaneously. The general  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Date** & **Platform/Sensor** & **Time** \\ \hline
25 January 2019 & Landsat/GOCI & 10:30 \\
14 March 2019 & Landsat/GOCI & 10:30 \\
15 April 2019 & Landsat/GOCI & 10:30 \\
22 September 2019 & Landsat/GOCI & 10:30 \\
8 October 2019 & Landsat/GOCI & 10:30 \\
25 November 2019 & Landsat/GOCI & 10:30 \\
11 December 2019 & Landsat/GOCI & 10:30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data list of Landsat 8 OLI and GOCI used in this study.  
Figure 1: Study area: Changhai County and Shicheng Island in the northern part of the North Yellow Sea.  
process of this study can be summarized in the following steps, and Figure 2 shows the basic workflow.  
Data preprocessing: According to the input requirements of the ESTARFM, the GOCI and Landsat data in the study area were resampled and matched point by point with each other. Other preprocessing measures were atmospheric correction and geometric correction.  
Data fusion: The ESTARFM and the modified model (ESTARFM_P) were used to fuse the preprocessed data to obtain high-spatiotemporal-resolution Chl-a.  
Result evaluation: Taking the actual observations of Landsat and GOCI on the target date as true values, the results of the two models were evaluated and compared.
# A Bibliometric Analysis of Soccer Biomechanics
## 4 Bibliometric Analysis
### Clustering
Keywords like \"GPS\", \"motion analysis\", and \"biomechanical phenomena\" highlight the use of sophisticated tools to monitor and analyze player movements. \"Return to sport\", \"performance\", and \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Name of Cluster** & **Items** & **Keywords** \\ \hline Cluster 1: “Physical Performance and Injury Prevention” & 27 & ACL reconstruction, adolescent, asymmetry, athletic performance, ball velocity, countermovement jump, electromyography, fatigue, force, force platform, gender, hamsstrings, isokinetic, knee injury, landing, muscle strength, peak torque, physiology, power, prevention, reliability, running, soccer, strength, torque, valgus, youth sports \\ \hline Cluster 2: “Footwear and Surface Interaction” & 21 & ACL injury, ankle sprain, anticipation, artificial turf, athletic injuries, cleats, femorocatedabular impingement, foot, football, gait, gender differences, groin, groin pain, hip, kick, neuromuscular control, perception, plantar pressure, sports medicine, traction, velocity \\ \hline Cluster 3: “Injury Risk and Screening” & 19 & ACL, adolescents, ankle, athletes, change of direction, female, ground reaction forces, injury prevention, injury risk, instep kick, knee, knee injuries, knee valgus, risk factors, screening, sport, stress fracture, youth \\ \hline Cluster 4: “Head Injuries and Monitoring” & 17 & Accelerometer, balance, brain injury, concussion, female athletes, head impact exposure, head kinematics, heading, inertial measurement unit, lower extremity, mild traumatic brain injury, mouthpiece, postural control, repetitive head impacts, subconcussion, team sports, wearable sensors \\ \hline Cluster 5: “Training and Rehabilitation” & 16 & Ankle injury, athlete, biomechanics, exercise, hamsstring, hamstring injury, injury, injury mechanism, jumping, kicking, magnetic resonance imaging, motor control, rehabilitation, sports, sports injury, training, video analysis \\ \hline Cluster 6: “Performance Metrics and Movement Analysis” & 14 & Agility, cutting, EMG, ground reaction force, injury, joint moment, kinematics, kinetics, knee joint, sports performance, sprinting, three dimensional, turning, vertical jump \\ \hline Cluster 7: “Advanced Biomechanical Analysis and Return to Sport” & 13 & Acceleration, ACL injury prevention, association football, biomechanical phenomena, coordination, deceleration, GPS, knee abduction moment, motion analysis, performance, return to sport, stiffness, technique \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Categorization of key research themes in soccer biomechanics based on co-occurrence analysis. \"technique\" indicate a focus on applying biomechanical insights to help athletes recover from injuries and enhance their on-field performance.
## 3 Results
### Spectral Analysis of Dandelion Inulin
#### 3.3.4 NMR Spectra of Dandelion Inulin  
\({}^{1}\)H and \({}^{13}\)C NMR spectra of dandelion inulin isolated by MAE and UAE are presented in Figure 5. NMR spectra of chicory inulin were used as a reference (Figure 5e,f).  
Figure 4: FTIR spectra of dandelion inulin obtained by different extraction techniques, where (a) classical extraction, (b) UAE, (c) MAE, and (d) chicory inulin Raftiline HPX.  
Figure 3: HYSEC-RID elution profile of dandelion and chicory inulin, where 1: UAE dandelion inulin, 2: MAE dandelion inulin, and 3: reference chicory inulin Raftiline HPX (DP = 25).  
Figure 5: Cont.  
\({}^{1}\)H NMR (500 MHz, D\({}_{2}\)O) \(\delta\) 5.33, 4.99, 4.68, 4.15, 4.00, 3.92, 3.81, 3.75, 3.67, 3.61, 3.45, 3.37 (MAE).  
The \({}^{1}\)H-NMR spectrum (Figure 5a) of dandelion inulin demonstrated characteristic shifts corresponding to the skeleton protons of a fructose ring at \(\delta\) 4.15 (H3f) and \(\delta\) 4.00 (H4f), in the region from 3.37 to 3.92 (H1f, H5f and H6f), and one isolated resonance for the single anomeric \(\alpha\)-glucose proton (H1g) at 5.33 (5.44) ppm. The integration ratio between 3.3 ppm and 4.2 ppm of fructofuranose and H-1 of glucopyranose could be used to calculate the degree of polymerization of inulin-type fructans as 20, while UAE gave DP 25 (Table 2). In the \({}^{13}\)C NMR spectra of dandelion inulin (Figure 5b,d), chemical shifts typical of only fructose units were observed. The \({}^{13}\)C NMR spectra of the dandelion inulin obtained after microwave-assisted extraction are presented in Figure 4b): \({}^{13}\)C NMR (126 MHz, D\({}_{2}\)O) \(\delta\) 103.10 (Cf2), 80.95 (Cf5), 76.85 (Cf3), 74.15 (Cf4), 64.16, 62.00 (Cf6), 60.74 (Cf1) ppm, while the UAE spectra are presented in Figure 5d): \(\delta\) 103.21 (Cf2), 81.04 (Cf5), 76.95 (Cf3), 74.24 (Cf4), 62.09 (Cf6), 60.86 (Cf1) ppm. These spectra contain prominent shifts for the C1-C6 carbons of fructosyl residue due to fructose repeated units. The \({}^{13}\)C shifts from glucose were not observed (Figure 2b,d) due to its superposition. A similar observation was reported for inulin from other plants [14; 27]. However, in the \({}^{13}\)C NMR of chicory inulin, glucose units were observed together with fructose units (Figure 5f): \(\delta\) 103.24 (Cf2), 81.07 (Cf5), 76.96 (Cf3), 74.24 (Cf4), 62.12 (Cf6), 60.86 (Cf1) ppm and carbon from glucose units: 92.48 (C1g), 73.92 (C2g), 74.00 (C3g) 72.22 (C5g), 71.31 (C4g), 69.24 (C6g) ppm. In all the \({}^{13}\)C NMR spectra (Figure 5), only one shift at 103.10-103.2 ppm was observed, corresponding to the C-2 carbon involved in \(\beta\)-(2\(\rightarrow\)1)-D-fructofuranosyl-fructose bonds. The data from the \({}^{1}\)H and \({}^{13}\)C NMR spectra confirm the chemical structure of dandelion roots composed mainly of fructose units linked with \(\beta\)-(2\(\rightarrow\)1) bonds and of only one terminal-linked glucose unit \(\alpha\)-(1\(\rightarrow\)2).
## 1 Introduction
Depending on the cavity geometry, soliton lasers (which include Kerr-lens mode-locked lasers as well as some fiber lasers) can emit multiple pulses per round trip in certain circumstances [13; 14]. Many of these lasers have operating regimes of stable multiple pulsing in which the pulse separations and relative phases do not vary [15]. However, in between there are also transition regimes: for example, pulses can undergo interesting evolutions from stable single-pulsing to multi-pulsing over several round trips through the laser [16]. Slight variations in the pump power can easily cause added instability. Sinusoidally-varying and chaotic pulse separations have been reported, as well as interesting phenomena such as soliton explosion [17]. Much more commonly and importantly, simply over-pumping a laser (which is difficult to avoid when attempting to maximize the laser output pulse energy) often results in satellite pulses. Given that many lasers can have such interesting internal dynamics and complex pulse shapes in certain situations, it is important to ensure that pulse measurement techniques are able to reveal such instabilities in pulses. As mentioned earlier, simulations of fluctuating pulse shapes have demonstrated that several measurement techniques based on interferometry have considerable difficulty detecting instability and yield only the coherent artifact [7; 8]. This is alarming, especially because several popular techniques, including SPIDER, display these problems and so conceal potential instability from the user and can report, for example, pulse lengths considerably shorter than are in fact present. Analysis in previous papers [8] suggested that even though some forms of instability would be invisible to SPIDER, multiple pulsing could be visible in a SPIDER measurement unless the pulse separation varied over a large range. In contrast, here we will demonstrate that, even if the pulse separation is stable, SPIDER also cannot see satellite pulses if their relative phase varies by \(2\pi\) -- a much more likely scenario. In addition, we perform various simulated measurements of unstable double pulsing using SPIDER and second-harmonic-generation (SHG) FROG to better understand the effects of unstable multi-pulsing on measurements made using these techniques. We find that SPIDER cannot see satellite pulses at all unless their phase variation is small. SHG FROG (henceforth referred to in this publication as simply FROG) under-estimates the amplitude of the satellite pulse, but it sees it, and we show that simple additional post-processing can be used to determine its approximate actual amplitude. Alas, because there is no indication of multi-pulsing in SPIDER, such post-processing is fundamentally not possible in SPIDER, and a separate FROG or autocorrelation measurement is necessary to reveal multi-pulsing when SPIDER is used for pulse measurement. We begin by reviewing analytical descriptions of measurements of unstable pulses in SPIDER and FROG.
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 4 Results and Discussion
### Experimental Setting  
This section describes the experimental setup and hyperparameter selection. In this context, the training procedure was carefully fine-tuned using the Adam optimizer, incorporating a progressive learning rate schedule of 0.001, suitable batch sizes of 32, and the careful selection of 50 epochs to guarantee convergence while avoiding overfitting. To implement the proposed model, we used a high-performance NVIDIA GeForce RTX 3090 GPU, enabling the execution of efficient training processes. In the implementation and experimentation, we used a prominent deep-learning framework, TensorFlow-banked kernels. The experimental settings employed in this study were carefully planned to support a rigorous evaluation of the ConvNeXt-based model coupled with its potential transformative influence on early skin cancer diagnosis. The main objective of the proposed model is to detect the affected skin area at an early stage. To this end, an automatic intelligent model is highly desirable to assist the dermatology department and patients before the cancer reaches its final level. Therefore, a deep learning technique was developed that learns significant features from diverse skin cancer diseases and identifies the types of illnesses. To tune the pretrained model, we removed the upper layer and updated the remaining layer parameters by adjusting our skin dataset. In the initial epoch, the model attempted to learn new patterns and update randomly assigned weights to minimize errors. Next, through backpropagation, the model randomly updated the weight values and quickly obtained the local minima using various filtering strategies and fusion over variant positions.
## 1 Introduction  
Climate change is well on its way to wreaking havoc. The application of renewable energy is a way of stopping it in its tracks. A huge increase in the demand for renewable energy is expected as the world moves to a carbon-neutral economy. Offshore wind energy is a highly effective way to meet this rising demand, but the sites suitable for the installation of bottom-fixed offshore wind turbines are fairly limited when set in relation to the available ocean area. By means of floating wind turbine systems, more areas can be used for deployment, but still, there are limits.  
Thus, there is a need to investigate ways to harness energy from areas further away from the shore and in the high seas (also known as international waters). The idea of a Sailing Renewable Energy Conversion (SailIREC) System is born from this need. A SailIREC System is proposed as a technological system capable of harvesting different renewable energy sources, including their conversion and subsequent storage. It is envisioned as an autonomously operating sailing vessel. Such a solution would come with the following advantages over a moored-floating or bottom-fixed wind turbine:* **Fast realisation and deployment:** The SailREC System could be licensed as a ship and operate in international waters provided that favourable legislation is developed in the future under the United Nations Convention on the Law of the Sea (UNCLOS) [1]. Thus, fewer permits and authorisations might be needed;
* **Cost-effectiveness:** The design of a SailREC System can be optimised for mass production, causing economies of scale to take effect;
* **Small footprint:** Subsea infrastructure, e.g., grid connection and mooring lines, is not required;
* **Tool for energy security:** A SailREC System could be used by countries with limited energy resources within their territorial boundaries.  
The idea of harnessing energy for areas further away from the shore is not completely new. The state of the art is briefly described in Section 1.1, while the objectives and structure of this paper are outlined in Section 1.2.
## 2 Materials and Methods
### Base Substrate, Sample Dimensions, and Mechanical Pre-Treatments
The tool itself exerted a force of approximately 2 N, which was accounted for within the \(\pm\)5 N tolerance. The applied pressure, and consequently the spring compression, was adjusted using the quill feed of the milling machine. This methodology has been previously validated in experimental studies, with findings reported in [18; 20]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Property** & **Synthetic Corundum** & **Steel Grit** \\ \hline Hardness & 9 Mohs & 390–530 HV \\ Melting Point, \({}^{\circ}\)C & 2040 & 1530 \\ Bulk Density, kg\(\cdot\)m\({}^{-3}\) & 1800 \(\pm\) 300 & 7000 \(\pm\) 60 \\ Grain Shape & Angular & Spherical \\ Grain Size Designation & F40 (355–500 \(\upmu\)m) & S170 (350–710 \(\upmu\)m) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**Compound, wt.\%** & **Chem. Element, wt.\%** \\ \hline \multirow{4}{*}{Chemical Composition} & Al\({}_{2}\)O\({}_{3}\) & 99.52 & C & 0.80–1.20 \\  & TiO\({}_{2}\) & 0.07 & Si & 0.40–1.50 \\  & SiO\({}_{2}\) & 0.17 & Mn & 0.60–1.20 \\  & Fe\({}_{2}\)O\({}_{3}\) & 0.07 & S & max. 0.05 \\  & MgO & 0.01 & P & max. 0.05 \\  & CaO & 0.02 & Fe & Remainder \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Physical properties and chemical composition of blasting media. The three mechanical pre-treatment methods employed in this study differ significantly in their technological impact on the steel substrate:  
* Steel shot blasting induces significant plastic deformation, creating a surface morphology characterized by intersecting spherical depressions. * Synthetic corundum blasting also results in notable plastic deformation; however, the impact on subsurface layers is less extensive due to the lower kinetic energy of angular abrasive particles. The interaction between the sharp-edged abrasive and the substrate produces distinct indentations and protrusions. * Grinding predominantly involves material removal, with only minimal plastic deformation. Unlike blasting, plastic deformation in grinding is negligible, particularly when using new abrasive tools, as was the case in this experiment.