esa-sceva/satcom-chunk-collection huggingface dataset:
# Contact Melting of Metals Explained via the Theory of Quasi-Liquid Layer
## 1 Introduction  
The processes underlying the appearance and growth of the intermediate phases that occur during the alloying of various metals to obtain intermetallic compounds and heterogeneous mixtures have not yet been fully studied. Moreover, of particular theoretical and practical interest is the clarification of the conditions required for mass transfer to occur at the boundaries of coexisting phases.  
Contact melting is a phenomenon in which two metals that are initially in a solid state begin to melt upon contact. The reason for this melting is that metal atoms diffuse into each other; as a result, the melting temperature of such a solution decreases. This phenomenon is characteristic of metals with a eutectic phase, in which the melting point of the mixture is lower than the melting point of the individual pure substances.  
The contact melting rate has yet to be fully elucidated. According to experimental data [1; 2; 3; 4; 5; 6; 7], contact melting of samples occurs on a timescale of seconds. However, in explaining this characteristic time, a contradiction arises. Indeed, in order for metal atoms to diffuse into each other, both metals must already be liquid, since the diffusion rate in a solid is negligible.
## 5 Discussion  
In this section, the authors will focus on discussing some limitations of the DST-LEO satellite-traffic prediction model as well as methods for improving the model to make its predictions more accurate or the model more lightweight. First, the DST-LEO satellite-traffic prediction model achieved excellent prediction results. The team led by the authors is ready to deploy the model at the gateway station in the low Earth orbit satellite Internet system for satellite-to-ground joint testing. The gateway station is equipped with various powerful servers, and the computing power conditions support the deployment of the model. However, the most ideal scenario is still to directly deploy the model on the satellite. Since the model is not extremely large, even if the resources on the satellite are limited, deployment can still be achieved by means of distributed computing and other methods. This is the ideal direction for future attempts at model deployment.  
Data-related limitations: The dataset of the internal low Earth orbit constellation business simulation system is too clean, as there may be a large number of noise, missing values, and incorrect records in the real data. Additionally, the existing data collection systems may not comprehensively cover all time and space ranges of the low Earth orbit (LEO) constellation. For example, the traffic data in some remote areas may be insufficiently collected.  
Model-related limitations: Although the model successfully completed the designed experiments, there is still room for improvement in its structure. For instance, the teacher-forcing mechanism has provided the authors with a good idea. Additionally, according to the long-term and short-term predictions, a model with a simpler structure can be selected for short-term prediction training, which is easier to deploy and consumes fewer resources.  
Future research directions: Techniques such as parallel computing and distributed computing can be adopted to improve the model's processing efficiency. The ultimate goal of the authors' research on traffic-related issues is to design a large-scale model suitable for the satellite communication field. By invoking various top-level small models, different downstream tasks in various scenarios can be completed to achieve system intelligence. For a low Earth orbit constellation system, computing power and memory resources pose significant challenges. In the follow-up, the authors will focus on researching more lightweight models while ensuring the model's performance, aiming to achieve relevant communication and monitoring functions at the lowest cost.
## 7 Analysis of the Influence of GTPS Operating Modes on the Power Quality
### Operating Mode No.1  
The GTU-1, GTU-2, GTU-3, GTU-4, and LM-1, LM-2, LM-3 are in operation. Two busbars of CSGM-10 kV and 10 kV busbar of the SS 35 kV \"Ilyichevskaya\" are switched on parallel operation.  
In this mode, it is possible to supply electricity to the main power system and (or) ensure uninterrupted power supply to oil and gas field loads connected to the 10 kV busbar of the SS 35 kV \"Ilyichevskaya\".  
The most similar modes were observed (Table 6):  
1. From 16 July 00:35 pm to 17 July 8:20 am (the GTU-3 (cell 13) is disconnected). In the considered time interval, the PQI did not exceed the normally permissible limits (Figure 3). According to the Operational Log, from 17:40 to 18:30 on 16 July, there was an exit from the operating mode due to a spontaneous power reset by the LM-3 (2 MW). During this emergency disturbance, the PQI did not go beyond the permissible values, except for the frequency deviation (which is within the permissible time limits), which indicates the high stability of this mode.
2. From 04:35 pm on 27 July to 02:30 pm on 29 July (the GTU-2 (cell 4) is disconnected). In the considered time interval, the PQI did not exceed the normally permissible limits (Figure 4). Small voltage fluctuations with a duration of less than 5 min, observed during the operation mode, can be caused by the BCM-4 control system operation from 11:50 am to 06:30 pm.  
Figure 3: The PQI profile on 16 July from 11:40 am to 8:30 pm. (**a**) Non-sinusoidal coefficient, (**b**) phase voltage in the secondary measuring circuit, (**c**) negative and (**d**) zero-sequence voltage unbalance and (**e**) frequency.
## 2 Sensor Structure and Measurement Principle
### Absolute Measurement Principle
We also note from Figure 2c that \(T=NT_{p}\), and \(mT_{p}\) can be employed to calculate the number of periods, \(M\), that the induction electrode array has moved relative to the stationary excitation electrodes as follows:  
\[M=\ \text{int}((\frac{mT_{p}}{NT_{p}}L)/W), \tag{7}\]  
where the operator \(\text{int}(\cdot)\) returns the integer value of its argument. Therefore, the relationship between \(x\) and \(\Delta t\) can be defined as follows:  
\[x=\ W(M+\frac{\Delta t}{T})\ =\ W(\text{int}((\frac{m}{N}L)/W)+\frac{n}{N}), \tag{8}\]  
Figure 3 presents the phase curves of \(U_{row1}\) and \(U_{row2}\) within their periods \(W_{row1}\) and \(W_{row2}\) with respect to displacement \(x\) obtained for \(L\) = 60 mm, \(W_{row1}\) = 10 mm, \(W_{row2}\) = 12 mm. According to Figure 3a, the phases of the two signals are distributed in a sawtooth form according to the value of \(x\) in each period. We also note that the phase curve of \(U_{row1}\) becomes increasingly advanced relative to that of \(U_{row2}\) with increasing \(x\). Additionally, until \(x\) = 600 mm, the phase of row 2 differs by exactly a period from that of row 1. From Figure 3b, we note that the phase difference arising between the two signals with increasing \(x\) is monotonic and very nearly linear. As such, positioning can be realized according to the phase difference.
## 4 Experimental Results and Analysis
### Performance Analysis and Comparison  
This section presents the performance analysis of the conceived method. The IP102 dataset is adopted as a test bench for models performing a challenging task on images with a complex background, different light conditions, and multi-distances and multi-angles views. The results of the detection and classification model and the classification-only architecture are compared to state-of-the-art models from the literature.
## 3 Numerical Results
### Microscale Analysis
#### 3.1.1 Convergence Study
The primary factor contributing to this lack of smoothness lies in the adopted integration scheme. To prioritise computational efficiency and flexibility, the NNRPIM formulation employs a single integration point within each sub-quadrilateral area of each Voronoi cell (recall Section 2.3 for details). While this approach may not significantly impact the average accuracy of the variable fields themselves, it does compromise its smoothness. As demonstrated in Figure 7, employing a higher-order integration scheme, such as the \(2\times 2\) Gauss-Legendre integration detailed in Section 2.3, leads to a marked improvement in the smoothness of the stress fields. The stress distribution as improved significantly, being now more smooth and continuous (the granulated distributions has disappeared). Comparing the results obtained with NNRPIMv1 (Figure 6g-i) and the NNRPIMv1* version with an higher integration order (Figure 7a-c), it is possible to visualise significant differences in the smoothness of the solution. Such a smoothness difference is more pronounced for the NNRPIMv2 formulation. Comparing the NNRPIMv2 stress distributions of Figure 6j-l with the NNRPIMv2* stress distribution (Figure 7d-f), it is possible to understand the effect of considering more integration points. Although using a higher-order integration scheme demonstrably enhances the smoothness of the stress field (and potentially other variable fields), its impact on the homogenised material properties appears not to be relevant. Table 3 shows the results obtained with the NNRPIMv1* and NNRPIMv2* versions (with an higher integration order), and comparing such results with the solutions presented in Tables 1 and 2, it is possible to observe that the homogenised values are not significantly affected by the integration scheme. While the enhanced smoothness achieved with the higher-order integration scheme is desirable, it comes at the expense of increased computational cost. Employing a single integration point per sub-quadrilateral area within each Voronoi cell allows for significantly faster analysis (approximately 10 \(\sim\) 15 times faster) compared to the \(2\times 2\) integration scheme. This becomes particularly relevant for computationally demanding problems, such as nonlinear analyses. Consequently, the subsequent examples will continue to use the single integration point approach to maintain computational efficiency. Figure 6: Von Mises equivalent stress distribution for the formulations considered: FEM-6n (**a–c**), FEM-3n (**d–f**), NNRPIMv1 (**g–i**), and NNRPIMv2 (**j–l**). The results corresponding to \(\tilde{\textbf{t}}_{100}\) are shown in (**a**,**d**,**g**,**j**), the results corresponding to \(\tilde{\textbf{t}}_{010}\) are shown in (**b**,**e**,**h**,**k**), and the results corresponding to \(\tilde{\textbf{t}}_{001}\) are shown in (**c**,**f**,**i**,**l**).
# Emerging Technologies for Injury Identification in Sports Settings: A Systematic Review
###### Abstract  
Sport injury recognition is rapidly evolving with the integration of new emerging technologies. This systematic review aims to identify and evaluate technologies capable of detecting injuries during sports participation. A comprehensive search of PUBMED, Sport Discus, Web of Science, and ScienceDirect was conducted following the PRISMA 2020 guidelines. The review was registered on PROSPERO (CRD42024608964). Inclusion criteria focused on prospective studies involving athletes of all ages, evaluating tools which are utilised to identify injuries in sports settings. The review included research between 2014 and 2024; retrospective, conceptual, and fatigue-focused studies were excluded. Risk of bias was assessed using the Critical Appraisal Skills Program (CASP) tool. Of 4283 records screened, 70 full-text articles were assessed, with 21 studies meeting the final inclusion criteria. The technologies were grouped into advanced imaging (Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DFI), and Quantitative Susceptibility Mapping (QSM), with biomarkers (i.e., Neurofilament Light (NFL), Tau protein, Glial Fibrillary Acidic Protein (GFAP), Salivary MicroRNAs, and Immunoglobulin A (IgA), and sideline assessments (i.e., the King-Devick test, KD-Eye Tracking, modified Balance Error Scoring System (mBESS), DETECT, ImPACT structured video analysis, and Instrumented Mouth Guards (iMGs)), which demonstrated feasibility for immediate sideline identification of injury. Future research should improve methodological rigour through larger, diverse samples and controlled designs, with real-world testing environments. Following this guidance, the application of emerging technologies may assist medical staff, coaches, and national governing bodies in identifying injuries in a sports setting, providing real-time assessment.  
injury monitoring; sport injury; emerging technologies; concussion; mild traumatic brain injury; wearables
## 4 Bifurcation Analysis and Control Performance
Figure 6 shows the steady-state vibration amplitudes of both the rotor system (\(a_{1}\) and \(a_{2}\)) and the connected LIPPF-controller (\(a_{3}\) and \(a_{4}\)) along the \(\sigma\)-axis at different values of \(\sigma_{1}=\sigma_{2}\) when \(f=0.015\). It is clear from the figure that the optimum working condition of the LIPPF-controller occurs along the dashed red line shown in Figure 6a,b (i.e., when \(\sigma=\sigma_{1}=\sigma_{2}\)), where \(a_{1}=a_{2}\approx 0\). Therefore, by substituting the condition \(\sigma=\sigma_{1}=\sigma_{2}\) into Equation (55), one can report that the optimum vibration suppression efficiency of the LIPPF-controller occurs when setting its natural frequencies (\(\omega_{1}\) and \(\omega_{2}\)) to become the same value as the rotor angular speed (\(\Omega\)). Based on Figure 6, the detuning parameters \(\sigma\), \(\sigma_{1}\), and \(\sigma_{2}\) were utilized as one bifurcation parameter via letting \(\sigma=\sigma_{1}=\sigma_{2}\) as shown in Figure 7, where the oscillation amplitudes of both the rotor system and the LIPPF-controller were plotted against the bifurcation parameter \(\sigma=\sigma_{1}=\sigma_{2}\) at different values of the rotor eccentricity \(f\). It is  
Figure 6: Vibration amplitudes of the rotor system and LIPPF-controller using \(\sigma\) as a bifurcation parameter at various values of \(\sigma_{1}=\sigma_{2}\) when \(f=0.015\): (**a**) vibration amplitude in \(X_{1}\) direction, (**b**) vibration amplitude in \(X_{2}\) direction, and (**c,d**) vibration amplitudes of the LIPPF-controller. clear from the figure that the rotor oscillation amplitudes (\(a_{1}\) and \(a_{2}\)) were suppressed to very small values along the \(\sigma=\sigma_{1}=\sigma_{2}\) axis regardless of the magnitude of the rotor eccentricity \(f\), where the excessive vibration energy of the rotor system was channeled to the LIPPF-controller via the energy bridge that was established between the rotor and the controller when tuning \(\omega_{1}\) and \(\omega_{2}\) to be the same values of \(\Omega\). Accordingly, it is possible to eliminate the lateral vibrations of the considered system regardless of the rotor angular speed \(\Omega\) and its eccentricity magnitude via making the introduced LIPPF-controller an adaptive controller. The Adaptive Linear Integral Positive Position Feedback controller (ALIPPF-controller) can be implemented from the engineering point of view via measuring the rotor angular speed \(\Omega\) to be fed into the digital computer to update the controller's natural frequency \(\omega_{1}\) and \(\omega_{2}\) to the same value of the measured \(\Omega\), instantaneously. Figure 7: Vibration amplitudes of the rotor system and ALIPPF-controller using \(\sigma=\sigma_{1}=\sigma_{2}\) as a bifurcation parameter when \(f=0.005,\ 0.01,\ 0.015,\ 0.02\), and \(0.025\): (**a**) vibration amplitude in \(X_{1}\) direction, (**b**) vibration amplitude in \(X_{2}\) direction, and (**c**,**d**) vibration amplitudes of the LIPPF-controller.
## 3 Methodology
### Segment Size  
The number of SLM segments, \(N\), is directly proportional to the maximum ideal enhancement of the refocused spot on the CCD. The parallel wavefront modulation method used to measure the RM requires \(4N+8\) intensity measurements [17]. For all the experimental RM measurements, the SLM was divided into 1024 equal-sized segments, arranged \(32\times 32\), which required 4104 intensity measurements to measure the RM. This was the largest number of segments that could be equally sized and utilized the entire SLM area.  
The segment size of the CCD also affects enhancement. Parallel wavefront modulation measures the RM in two separate halves. If the CCD segment size is too large, the two halves of the RM may focus on separate locations within the same CCD segment, resulting in a larger blurred spot with lower overall enhancement. The only indication that a CCD segment is too large is the lack of enhancement increase during the third step of parallel wavefront optimization of phase matching the two halves of the RM. Selecting a CCD segment size that is smaller than necessary increases the total number of segments and increases the memory and processing time of the RM measurement. The best CCD segment size was found to be approximately \(\
icefrac{{1}}{{2}}\) the size of the predicted diffraction-limited spot of the optical system.  
The predicted spot size for the optical system shown in Figure 1 was calculated in the original reflective inverse diffusion proof-of-concept experiments [7]. The radius of the final spot was given by  
\[q_{2}\approx\frac{z_{2}}{f}\frac{D_{SLM}}{2\sqrt{N}}, \tag{7}\]  
where \(z_{2}\) is the distance from the sample to the CCD, \(f\) is the focal length of lens \(L_{1}\), \(D_{SLM}\) is the width of the SLM, and \(N\) is the total number of SLM segments. Using Equation (7), the CCD segment size for the focal-plane optical setup is 120 \(\upmu\)m, which is \(16\times 16\) pixels on the Thorlabs 40740M CCD.  
The predicted spot size for the imaging system in Figure 2 is calculated as the diffraction-limited spot produced by an aperture the size of the demagnified SLM image that is applied to the rough-surface reflector. The radius of the final spot is then given by,  
\[q_{2}\approx\frac{\lambda z_{2}}{\frac{s_{o}}{s_{o}}D_{SLM}}=\frac{s_{o}}{s_{i }}\frac{\lambda z_{2}}{D_{SLM}}, \tag{8}\]  
where \(s_{o}\) is the object distance from the SLM to lens \(L_{1}\), and \(s_{i}\) is the image distance from lens \(L_{1}\) to the rough-surface sample. Thus, the segment size for the imaging system is 317 \(\upmu\)m, which is \(42\times 42\) pixels on the Thorlabs 4070M CCD.
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 4 Results and Discussion
### Single-View Consistency Evaluation
#### 4.1.1 Subjective Evaluation  
Five pairs of images before and after color consistency processing are shown in Figure 9. It can be seen that the proposed approach was able to obtain good single-view color consistency and rich image tone and contrast information. There was noticeably uneven light in Figure 9a, which was rich in red and bright in the lower-right part and rich in blue and dark in the upper-left part. The land presented different colors and brightness due to the uneven lighting. The proposed method was able to eliminate the effects of the uneven lighting, and the land part presented reasonable brightness and color. In addition, the image gradation was well preserved, which facilitated the identification of scenes. There was also uneven lighting in Figure 9c,e, where the brightness of the image was gradually reduced due to changes of the solar elevation. The left part of Figure 9c and the lower-right part of Figure 9e were brighter than the other parts. The proposed approach eliminated the change of lighting and obtained a uniform image. Figure 9g,i were bluish in color, and the Antarctic continent in the images looked like blue ocean, which brought difficulties to the target identification. The proposed approach was able to restore the color of the Antarctic continent.  
Figure 9: _Cont._  
Figure 9: Comparisons between the original images and the result of the single-view color consistency method.
## 4 Results and Discussion
### Emulsion Stability  
The stability of surfactant-stabilized and Pickering O/W emulsions consisting of 50% by volume oil were monitored by visual observations over a period of seven days. The surfactant-stabilized emulsions were prepared using 0.5% (by volume) Triton X-100 (non-ionic surfactant) and the Pickering emulsions were prepared using 2% wt. starch nanoparticles, as described in the experimental work section. Photographs of the bulk emulsion samples contained in graduated cylinders were taken at different time intervals to study the phase separation behavior of emulsion under quiescent condition. The photomicrographs of emulsion samples were also taken using a Zeiss optical microscope equipped with a camera. A small quantity of an emulsion sample was diluted with the same continuous phase and was placed on a glass slide in order to take the photomicrographs.  
Figure 8 compares the images of fresh surfactant-stabilized and SNPs-stabilized O/W emulsions. Clearly, the droplets of the surfactant-stabilized emulsions are much smaller than the droplets of the nanoparticles-stabilized emulsion. The Sauter mean diameters of the surfactant-stabilized and nanoparticles-stabilized emulsions are 14 and 91 \(\upmu\)m, respectively.  
Figure 9 shows the effect of aging on the droplet sizes of emulsions over a period of 24 h. The surfactant-stabilized emulsions show a negligible change in the droplet sizes over the 24 h period (Figure 9A), while the nanoparticles-stabilized show a large increase in the droplet size over the same period of time (Figure 9B). Thus, surfactant-stabilized O/W emulsions are much more stable than the Pickering emulsions.  
Figure 8: Comparison of photomicrographs of fresh surfactant-stabilized (left photomicrograph) and nanoparticles-stabilized (right photomicrograph) O/W emulsions.  
Figure 10 shows the images of bulk samples for fresh, 24-h aged, and seven-day aged emulsions. Nanoparticle-stabilized emulsions creamed faster than the surfactant-stabilized emulsions. After seven days of aging, the surfactant-stabilized emulsions showed little change except for the upward rise or creaming of droplets. The nanoparticles-stabilized emulsions, however, showed noticeable coalescence and phase separation with aging.  
Figure 9: Effect of aging on the droplet size of (**A**) surfactant-stabilized and (**B**) nanoparticle-stabilized O/W emulsion.
## Appendix A  
Proof of Theorem 1.: Let \((\hat{\mathbf{W}},\hat{\theta})\) and \((\hat{\mathbf{W}},\hat{\theta})\) denote the optimal solutions of problems (P0) and (P1), respectively, and \(\gamma_{k}^{*}=2^{R_{k}^{*}}\) be the optimal achievable SINR corresponding to the optimal achievable rate \(R_{k}^{*}\). It can be easily verified that \((\hat{\mathbf{W}},\hat{\theta})\) is also a feasible solution of problem (P1). Thus it implies that  
\[\sum_{k=1}^{K}||\hat{\mathbf{w}}_{k}||^{2}\leq||\hat{\mathbf{w}}_{k}||^{2}\leq P _{T}, \tag{10}\]  
where the first inequality is derived from the fact that \((\hat{\mathbf{W}},\hat{\theta})\) minimises \(||\hat{\mathbf{w}}_{k}||^{2}\leq\) in problem (P1) for given \(\gamma_{k}^{*}\). On the other hand, the second inequality holds as \((\hat{\mathbf{W}},\hat{\theta})\) is a feasible solution of (P0). Thus the inequality (10) together with (9) and (10) imply that \((\hat{\mathbf{W}},\hat{\theta})\) is a feasible solution of (P0). Eventually, it yields  
\[\frac{|(\mathbf{h}_{r,k}^{H}\hat{\mathbf{\Theta}}\mathbf{G}+\mathbf{h}_{d,k}^ {\mathbf{H}})\hat{\mathbf{w}}_{k}|^{2}}{\sum_{j\
eq k}^{K}|(\mathbf{h}_{r,k}^ {H}\hat{\mathbf{\Theta}}\mathbf{G}+\mathbf{h}_{d,k}^{H})\hat{\mathbf{w}}_{j}| ^{2}+\sigma_{k}^{2}}\geq\gamma_{k}^{*},\forall k. \tag{11}\]  
Combining (11) and (9), it can be expressed as:  
\[\frac{|(\mathbf{h}_{r,k}^{H}\hat{\mathbf{\Theta}}\mathbf{G}+\mathbf{h}_{d,k}^ {\mathbf{H}})\hat{\mathbf{w}}_{k}|^{2}}{\sum_{j\
eq k}^{K}|(\mathbf{h}_{r,k}^ {H}\hat{\mathbf{\Theta}}\mathbf{G}+\mathbf{h}_{d,k}^{H})\hat{\mathbf{w}}_{j} |^{2}+\sigma_{k}^{2}}=\gamma_{k}^{*},\forall k. \tag{12}\]  
This concludes the proof by establishing a solution correspondence between problems (P0) and (P1).