esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Visualization of Coal Quality Indices Distribution  
The visualization distribution can clearly show the spatial differences of coal quality indices, which can provide an important reference for coal mining, washing and combustion processes. In this study, the LSSVR model with combined spectral feature wavelengths and texture features was employed on coal hyperspectral images to generate gray-scale images of coal quality indices. Samples representing the minimum, 25%, 75%, and maximum values of the dataset were selected for color stretching to illustrate the visual distribution and demonstrate the model's effectiveness in predicting samples with varying actual values.  
The distribution visualization results of coal samples with different quality indices are shown in Figure 10. The color gradient bar denotes the variation range of each indicator and assigns color to the grayscale image. It is evident that the distribution of predicted values for each index is well-balanced, ranging from blue for the minimum value to red for the maximum value, indicating that the model predicts unknown data within a range close to the actual values and exhibits strong generalization ability. Given that the true value range for FC is 29.12-54.34, the minimum value is notably larger than 0 compared with other indices, and its color representation is situated at the upper end of the color gradient bar. Significant variations are observed in the prediction results for samples with different true values, with notable color differences between minimum and maximum value samples. Conversely, the prediction results within each coal sample have small differences with a narrow range of color changes, which indicates that the model can accurately capture the sample characteristics and implement excellent prediction while maximizing the differences between different samples and minimizing the differences between the same samples. The true and predicted values for each sample are closely aligned, with the maximum differences of 0.78%, 1.02%, 0.21%, 6.06%, and 0.19 MJ/kg in Ash, VM, MC, FC, and CV, respectively, which is consistent with the accuracy performance of the model. Overall, the visualization can effectively illustrate spatial differences in coal quality indices and provide valuable technical references for coal mining, processing, and utilization.  
Figure 9: Relative contribution of coal quality indices based on mean absolute SHAP values.
# Besinc Pseudo-Shell Model Sources with Circular Coherence
## 1 Introduction  
The research for physically realizable partially coherent sources keeps on attracting a considerable interest, due to the advantages that partially coherent beams present over their coherent counterparts in some applications, such as, for instance, free space optical communications [1; 2; 3], imaging [4], and optical trapping [5; 6] (see also [7; 8] and references therein).  
In the coherence theory the Schell model [9; 10], describing sources with shift-invariant coherence properties, has played a crucial role. Schell-model sources can be also produced in a very simple way starting from spatially incoherent sources. Nonetheless, recent advances in different fields require new source models with specific properties to be provided. In this sense, the control of the coherence properties of the source allows to obtain beams with peculiar behaviour of their intensity in propagation [8; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23].  
In analogy to the Schell model, scalar sources that present shift-invariance only along either the radial or azimuthal coordinate have been recently proposed [18; 20; 24; 25]. Since their coherence properties are shift-invariant only along a polar coordinate, they have been named _pseudo-Schell model_ sources [20; 24]. Their cross-spectral density (CSD) can fulfill the non-negativeness condition [10; 26], in which case they represent physically realizable sources.  
The aim of this work is to analyze the behavior of a type of pseudo-Schell model sources whose coherence properties depend only on the difference of the radial coordinates of two points, so that they show circular coherence properties [15; 16]. The degree of coherence has been chosen as a besincfunction while a Laguerre-Gaussian profile has been selected for the intensity at the source plane. In this case, a pseudo-modal expansion of these sources has been derived. The intensity and coherence properties of the fields radiated from such sources has been described. For appropriate coherence parameters, a sharpening of the intensity profile occurs within a certain range of propagation distances. It has been shown that this sharpening effect could be exploited for simultaneous trapping of dielectric nanoparticles with either higher or lower refractive index than that of the surrounding medium.  
The paper is organized as follows: after this Introduction, in Section 2, a family of pseudo-Schell model sources is presented, its coherence properties are described and a pseudo-modal expansion is obtained; the paraxial propagation of the beams generated from such sources are studied in Section 3, while their trapping capabilities are analyzed in Section 4. The main results of this work are discussed in Section 5. Concluding remarks are summarized in Section 6.
## 5 Results and Discussion
### Instantaneous Throughput  
#### 5.1.1 Healthy Network  
A healthy network is taken to check the trend of instantaneous throughput. Figure 14 shows that instant throughput increases linearly; the value starts to grow from 10 s because of the time spent generating the packets before that value. After generating packets, the graph shows the linear relation and jumps to a maximum value of about 20,000 kbps at 200 s.
# A Wear Simulation Method for Mechanical Face Seals under Friction Instability Conditions
## 4 Validation of the Proposed Method  
The simulation results and experimental results presented in Ref. [4; 5] are used to verify the validity of the proposed mathematical model. The parameter values used in Ref. [4; 5] are listed in Table 1.  
The simulation is performed by decreasing the shaft's angular speed \(\Omega\)(t), which is shown in Figure 6a. The angular vibration \(\theta\)(t) of the stator and angular vibration speed \(\partial(t)\) of the stator are shown in Figure 6b,c, respectively. The simulation results of the total torque are shown in Figure 6d. It is observed that the simulation results are in good agreement with those in Ref. [4; 5].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Meaning** & **Value** \\ \hline C & axial damping & 2.0529 N\(\cdot\)s/m \\ C\({}_{\rm t}\) & torsional damping & \(6.5\times 10^{-5}\) kg\(\cdot\)m\({}^{2}\) \\ \(f_{\rm d}\) & dynamic friction coefficient & 0.05 \\ \(f_{\rm s}\) & static friction coefficient & 0.1 \\ \(f\) & moment of inertia & \(3.4\times 10^{-7}\) N\(\cdot\)m\(\cdot\)s/rad \\ \(K\) & axial stiffness & \(42142\) N\(\cdot\)m \\ \(K_{\rm t}\) & torsional stiffness & \(4.3090\) N\(\cdot\)m/rad \\ \(k_{\rm w}\) & wear modulus & \(0.35\times 10^{-6}\) mm\({}^{2}\)/N\(\cdot\)m \\ \(m\) & mass & \(0.01\) kg \\ \(  
The torsional damping ratio is defined as \(\xi_{t}=C_{t}/2\sqrt{K_{t}}l\), and the axial damping ratio is defined as \(\xi=C/2\sqrt{Km}\). During shaft deceleration, the friction coefficient changes, causing the damping ratio of the dynamic system to change from positive to negative. Thus, when instability occurs, the stator begins to oscillate.  
Considering the effects of the lubricated conditions, the wear modulus \(k_{w}\) was set as \(0.35\times 10^{-6}\) mm\({}^{3}\)/N-m; the value was obtained from wear tests [36]. This wear modulus is an average value, measured in the boundary conditions regime. If the relative angular speed \(\omega\) between the matching surfaces is constant at 100 rad/s, the shaft does not decelerate. The stator profile after the operation is obtained using Equation (18); the result is shown in Figure 7a, and the worn area is shown in Figure 7b. The stator profiles demonstrate that wear has occurred near the inner radius; this result is in agreement with the result obtained by [PERSON] [14].  
These results validate the proposed method. However, the difference between Gaussian and non-Gaussian surfaces and the change in viscosity with the temperature of the film need to be further discussed. In addition, the influence of the dynamic parameters, such as the axial stiffness, axial damping, torsional stiffness, and torsional damping on the seal is also analyzed.
## 3 Experimental Results and Analysis
### Evaluation Metrics  
In this study, we utilize Precision, Recall, Mean Average Precision (MAP) 50, and _mAP_50:95 as key metrics to evaluate our model's detection accuracy. Precision is defined as the proportion of true positive samples among those identified as positive by the model. Recall, also known as the true positive rate, measures the ratio of correctly predicted positive samples to the total actual positive samples. Both _mAP_50 and _mAP_50:95 serve as crucial metrics for assessing the model's proficiency in both localization and classification of detection objects. The _mAP_ is calculated as the average of the Average Precision (_AP_) for all categories at a given Intersection over Union (_IoU_) threshold. Specifically, _mAP_50 represents the mean accuracy across categories at an _IoU_ threshold of 0.5, while _mAP_50:95 indicates the mean accuracy at a more stringent _IoU_ threshold of 0.95. The formulae are shown below:  
\[P=\frac{TP}{TP+FP} \tag{7}\]  
\[R=\frac{TP}{TP+FN} \tag{8}\]  
\[AP=\int PdR \tag{9}\]  
\[mAP=\frac{1}{n}\sum_{j=1}^{n}AP(j) \tag{10}\]  
\[AP50=\frac{1}{n}P_{i}^{IOU=0.5}(R_{i}^{IOU=0.5}) \tag{11}\]  
\[AP50:95=\frac{1}{10}(AP50+AP55+\cdots\cdots+AP95) \tag{12}\]  
where \(n\) is the detection target type, _TP_ (True positives): the number of correctly classified positive samples, i.e., the number of samples that are actually positive and classified as positive by the classifier. _FP_ (False positives): the number of incorrectly classified positive samples, i.e., the number of samples that are actually negative but classified as positive by the classifier. _FN_ (False negatives): the number of samples incorrectly classified as negative, i.e., the number of samples that are actually positive but classified as negative by  
Figure 7: Example of MPDIoU Real-picture.  
the classifier. \(TN\) (True negatives): the number of samples correctly classified as negative, i.e., the number of samples that are actually negative and classified as negative by the classifier. The \(PR\) curve is obtained by taking the Recall value as the horizontal axis and the Precision value as the vertical axis. \(AP\) is the integral of the \(PR\) curve, i.e., the area enclosed by the curve; in the experiments of this paper. The size of the input image is 640 \(\times\) 640, in which the initial learning rate is 0.01. The optimizer is SGD, and the SGD momentum is 0.937. And the learning rate adjustment strategy is cosine annealing strategy, the number of iterations is 250 rounds and batch size is 16.
# Direct Solar Thermal Water-Splitting Using Iron and Iron Oxides at High Temperatures: A Review
## 2 Theoretical Background
#### Kinetic Model
The Fe oxidation kinetics follow a linear\(-\)parabolic behaviour in the presence of H\({}_{2}\)O\({}_{\text{(g)}}\), suggesting a transitional process wherein the diffusion of the hydroxyl ion is a determining factor during the oxidation [52]. [PERSON] et al. [56] indicated that the kinetic oxidation of iron at 1100 \({}^{\circ}\)C can be adjusted to a parabolic law during the first 3 h, after which a linear behaviour is established, increasing the weight per unit area of all specimens (the rate of weight gain was 6.2 mg/cm\({}^{2}\) per hour). The effect of temperature on the reaction rate is obtained by applying the Arrhenius equation, which is represented by the following expression [54,55]:  
\[K=K_{\text{o}}e^{\frac{-\Delta G^{\text{o}}}{RT}} \tag{10}\]  
where K is the equilibrium constant, K\({}_{\text{o}}\) is the reaction constant, \(\Delta\)G\({}^{\circ}\) is the activation energy, R is the gas constant, and T is the absolute temperature. If the reaction rate is governed by diffusion through the oxide layer in the solid state, the oxide thickness (rust) increases during the diffusion process. However, at the same time, the kinetics slow down over time [41]. In the cathodic subprocess, the reaction 2H\({}_{2}\)O\({}_{\text{(g)}}\) + 2e\({}^{-}\)\(\rightarrow\) 2 OH\({}^{-}\) + H\({}_{2}\) occurs as a product of the high-temperature dissociation process. The scientific literature indicates that the formation of OH\({}^{-}\) from H\({}_{2}\)O\({}_{\text{(g)}}\) is proportional to the concentration of H\({}_{2}\)O\({}_{\text{(g)}}\) and that the appearance of OH\({}^{-}\) cannot be interpreted in terms of the simple dissociation of H\({}_{2}\)O\({}_{\text{(g)}}\). It is necessary to consider the pathway involved in the formation of intermediate products generated during ORR such as (i) H\({}_{2}\)O\({}_{2}\) and (ii) HO\({}_{2}\)\({}^{-}\)[57].
## 2 In Vitro Models to Study CAC
### Skeletal Muscle Wasting
#### 2.1.2 L6 Cell Line  
The rat L6 cell line was established from rat skeletal muscle myoblasts isolated by [PERSON]. These cells are a common precursor model that has been used in muscular research, particularly constituting the main in vitro model for metabolic studies [46]. The complete and successful myotube differentiation process of L6 cells takes 5-6 days [48]. After that time, the cells become less stable lasting only 6 more days in culture, showing a reduction in myogenic differentiation 1 (MYOD1) and simultaneously creatine kinase M type (CKM) [46].  
L6 cells express lower levels of GLUT-1 and GLUT-3, the transporters responsible for baseline glucose uptake, in an expression pattern analogous to fully differentiated mammalian muscle. However, the model has higher expression levels of GLUT-4, the only insulin-dependent glucose transporter existing in skeletal muscle [46]. This suggests that L6 cells have a better response to insulin-stimulated glucose uptake even though the baseline glucose uptake is inferior [57]. Therefore, this suggests that L6 cells are the most promising candidate as an in vitro model system to investigate glucose uptake mechanisms, insulin resistance mechanisms and their interplay with other metabolic diseases [40; 46].  
Additionally, L6 myotubes express high levels of glucocorticoid receptors [58]. L6 cells do not develop organised sarcomere, instead, cells possess many contractile units forming myofibrils that easily differentiate in myotubes, giving the differentiated cells culture the appearance of a disorganised network [46; 48]. The myotubes formed by L6 do not contain \(\alpha\)-actin or muscle myosin II, neither exhibit any obvious contractile activity upon stimulation [46; 48]. Collectively, these constraints make them less indicated to study stimuli-muscle contraction. On the other hand, L6 cells present a force-Ca\({}^{2+}\) curve resembling the muscle in vivo [48; 49].  
To optimise the organisation of skeletal muscle tissue, L6 cells have been cultured in 3D micro-grooved scaffolds of collagen capable of orientating the alignment of myoblasts forming multi-layered cell sheets according to specific patterns. Such application has great potential in investigating cell-cell interactions, while maintaining the muscle development process and integrity [59].
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
## 3 Methods
### Single-Robot End-to-End Navigation
#### 3.2.2 Network Structure  
Owing to the structure of the 2D lidar data, we add one-dimensional, convolutional neural network (CNN) layers to the feature extraction part. To enrich the perception information of the policy module, some relative parameters about the target are added to our framework. It enables the policy module to simultaneously consider the sensor observation and the robot state information. Both of them are essential for efficient navigation.  
As shown in Figure 1, the raw lidar sensor data are filtered into 180 dimensions as inputs. As mentioned before, there are two kinds of neural networks in our module. One is the actor network and the other is the critic network. Both of them use three one-dimensional, CNN layers for feature extraction. After the data feature extraction, the networks use residual building blocks with shortcut connections [42] to reduce the training complexity. Then, we pack the target distance, the target angle and the actions at the last timestep as the state parameter set. The state parameter set and the lidar data feature extraction will be fused together and fed to the actor and critic networks at the same time. For the actor network, the fusion data will pass three fully connected layers and output the linear velocity and angular velocity through different activation functions. Besides controlling the robot navigation, the output of the actor network will be transmitted to the critic network. The output action, the state parameter set and the feature extraction of the critic network will be fused into one data stream. Afterpassing through four fully connected layers, the data stream will finally become a Q value to evaluate the policy and train the networks.
###### Abstract  
Background: Accurate partial weight-bearing (PWB) is essential for postoperative recovery after lower limb surgery, yet patients often fail to maintain load within clinically meaningful thresholds. Methods: In this pilot randomised controlled trial with 1:1 concealed allocation, 34 inpatients aged 18-85 who underwent femoral fracture fixation, hip arthroplasty, or knee arthroplasty were enrolled and followed for 14 days. Participants were randomly assigned to either standard physiotherapy or the same protocol with a low-cost tactile insole (\"incentiviser\") that provides mechanical feedback when the prescribed 20% body weight (BW) load is exceeded. The primary outcome was absolute deviation from target load, with a minimal clinically important difference (MCID) of \(\pm 2\) kg. Results: In total, 88% of the intervention group achieved the MCID at discharge versus 24% of controls. The between-group difference in final load error was 10.8 kg (95% CI: \(-15.2\) to \(-6.4\)), with a large effect size ([PERSON]'s d = 1.71). No significant differences were found in pain (NRS) or walking distance (6 MWT) between groups. Conclusions: The tactile incentiviser significantly improved PWB accuracy within 14 days, meeting MCID thresholds in most cases. Its low cost and simplicity make it promising for routine or home-based use. Limitations include the small sample, diagnosis heterogeneity, and absence of a sham control. Larger multicentre trials are needed to confirm generalisability and long-term clinical impact.  
physical therapy; rehabilitation; lower limb surgery; partial weight bearing +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Bioluminescence Inhibition Assay  
Lyophilized aliquots of _V. fischeri_ containing 3% NaCl were reconstituted with 1 mL of distilled water and re-suspended in 10-30 mL of the nutrient broth (NaCl 15 g, peptome 2.5 g, yeast extract 1.5 g, glycerol 1.5 mL, HEPES 0.01 M in 500 mL, pH 7). An amount of 200 \(\upmu\)L of the bacteria suspension and 100 \(\upmu\)L of each sample in distilled water plus 3% NaCl or blank (i.e., 3% NaCl in distilled water) were added to the microplate wells. The emitted light was recorded at various intervals between 0-48 h after preparation of the microplate. Each sample was replicated on five independent wells. The concentrations tested on this organism were in the \(\upmu\)g L\({}^{-1}\) range, precisely 100, 50, and 0.1 \(\upmu\)g L\({}^{-1}\), because of the high sensitivity usually displayed by this assay.  
The bioluminescence inhibition percentage (\(I\%\)) was used to express the toxicity of the tested samples and calculated according to the same equation employed for the algae, by using, in this case, the intensity of the emitted light (\(L\)):  
\[I\%=\frac{L_{control}-L_{sample}}{L_{control}}\times 100\]
## 3 Experiments and Results
### Accuracy of the Developed Classifier  
In order to verify the accuracy of the proposed classifier, the experiment was conducted on the 900 testing samples, which included 300 grape samples and 600 background samples. The classification results of the proposed classifier were compared with the weak linear classifiers, and the experimental  
Figure 6: The detection processes of grape clusters in Figure 3a. (**a**) Obtained binary image by using the strong classifier; (**b**) Obtained grape region image after eliminating noises and executing a morphological operation (blue point was the barycenter); (**c**) The contour of the detected grape clusters; and (**d**) The enclosing rectangle were drew in the original image.  
results were analyzed and counted by the true positive rate (TPR) and the false positive rate (FPR). A detailed comparison of the classification results is shown in Table 2.  
From the table we can see that the proposed classifier could achieve the highest accuracy, which reached up to 96.56%. In the weak classifiers, the total accuracy of the weak classifier \(h_{1}(x_{i})\) was the highest, reaching 93.67%, and the weak classifier \(h_{3}(x_{i})\) had the lowest, only 81.33%. The classification accuracy of all weak classifiers was lower than the strong classifier. By analyzing the results, it can be seen that the proposed classifier based on the AdaBoost framework improved the efficiency and accuracy of the classification, therefore it could be well used to identify the grape clusters from the complex background.
## 2 Materials and Methods  
### Data  
#### 2.1.1 FLUXNET2015 Data  
The FLUXNET2015 dataset includes sites with records over twenty years (from 1991 to 2014) and shares data on water, CO\({}_{2}\), and energy exchange between the atmosphere and biosphere, and synchronous meteorological and biological observations at the ecosystem scale from 212 global sites [35]. Considering the data availability and the observation duration, 45 forest sites (see Figure 1 and Table S1) were selected in which observations covered at least 5 years and could provide input parameters for all five LUE models. These sites included ENF (evergreen needleleaf forest), EBF (evergreen broadleaf forest), DBF (deciduous broadleaf forest), and MF (mixed forest) biomes. All in situ data were available at the FLUXNET2015 website, and data quality was controlled under a standardized data processing [35]. The daily VPD (Vapor Pressure saturation Deficit), sensible heat, air temperature, latent heat, CO\({}_{2}\) mole fraction, shortwave radiation, and remote sensing data were utilized for driving single LUE models. The missing data were eliminated using the quality control flags. The shortwave radiation (SR) was used to compute photosynthetically active radiation (PAR) as SR \(\times\) 0.45 [36]. Ground GPP products were used to validate single models and machine learning methods. After the data quality check, the average of nighttime and daytime ground GPP products was used to carry out validation at site and forest-type scales [34].  
Figure 1: Köppen–Geiger climate zones and 45 FLUXNET2015 forest sites (red triangles) distribution. Köppen–Geiger climate symbols are listed in Table S2 in the Supporting Information File.