esa-sceva/satcom-chunk-collection huggingface dataset:
# Feedback Characterization for Enhanced Heat Recovery from Composing Processes: A Review
## 3 Results and Discussion
### Energy-Related Properties
Reliable carbon sources include hardwoods, such as Acer macrophyllum (bigleaf map), with a carbon content of 49.64%, and softwoods, such as Sequoiadendron giganteum (giant sequoia), with a carbon content of 54.66% [38]. For animal-based materials, the mean C content was 477.61 g/kg TS, ranging from 388.0 g/kg TS in pig manure mixed with wheat straw [16] to 528 g/kg TS in spent pig litter [39] with low variability (CV = 0.11). This consistency makes animal-derived materialsa stable choice for C supply in heat recovery. The low variability of C content in animal-derived materials (CV = 0.11) indicates their suitability for consistent heat generation, making them valuable in compost systems that require predictable heat output. Non-agricultural feedstocks have a mean C content of 506.33 g/kg TS with moderate variability (CV = 0.11) with a minimum value of 434.0 g/kg TS for mixed paper [13]. Non-agricultural feedstocks offer a reasonable and consistent carbon source for aerobic processes and, if entirely composed of biomass, CHRSs. While it may be effective in combination with other feedstocks, regulatory considerations and variability in waste composition could affect its long-term viability as a primary carbon source. In particular, regulations often address concerns about contaminants, pathogens, and hazardous substances that may be present in non-agricultural waste streams, requiring strict monitoring and compliance with environmental safety standards. TOC is a key indicator of bioavailable organic matter, directly impacting heat generation during composing. Plant-based materials have a high mean TOC (422.91 g/kg TS), ranging from 308.0 g/kg TS in green waste, fruit, and vegetables [25] to 658.0 g/kg TS in biochar-enhanced mixes [30]. The moderate coefficient of variation (CV = 0.22) highlights the potential to adjust TOC levels with additives, optimizing heat output. Animal-derived materials show a slightly lower mean TOC (406.19 g/kg TS) with minimal variation ranging from 353.8 g/kg TS in pig slurry solids [21] to 450.0 g/kg TS in pig manure with saw-dust [40]. This consistency (CV = 0.08) suggests that animal-derived feedstocks are reliable TOC sources for stable heat generation. The steady TOC levels in animal-derived materials are advantageous for consistent heat production, making them suitable for applications where reliability is critical. However, their relatively lower TOC compared to plant-based materials could limit their standalone heat recovery potential. Non-agricultural feedstocks have the least mean TOC content of 243.73 g/kg TS with a limited range from 201.4 g/kg TS for a mixture of organic fraction of MSW, green waste, and paper [41] to 321.4 g/kg TS in mixed refuse [36], exhibiting moderate variability (CV = 0.18).
## 3 Health Monitoring Data of the HZMB Immersed Tunnel
### Monitoring Items  
To ensure the structural safety of the immersed tunnel, a monitoring system is installed in the representative and key areas to monitor the overall or local stress conditions of the tunnel in real time or periodically. The health monitoring system of the HZMB immersed tunnel consists of a hardware system and a software system. Among them, the hardware system includes various monitoring devices and sensors embedded in and between the elements, signal transmission cables and signal modulation and demodulation equipment. The data from the monitoring items are divided into two categories: structural response data and environmental data. The structural response data includes: ground motion, stress and strain, joint deformation and uneven settlement of element. The environmental data includes: temperature and humidity, structural temperature, and traffic loads.  
Figure 4: Cross-sectional geometry of the HZMB immersed tunnel.  
Figure 3: Longitudinal layout of the HZMB immersed tunnel.
## 3 Materials and Methods
### Emulation
As one can see, the prediction is obtained as a linear combination of weighted kernel (covariance) functions, the optimal weights given by \(w=(K+\sigma_{n}^{2}\mathbf{I})^{-1}\mathbf{f}(\mathbf{x})\). Many different functions can be used as kernels for [85]. We used the automatic relevance determination squared exponential kernel for GPR, which has a separate length hyperparameter for each input dimension. Stochastic gradient descent algorithms maximizing the marginal log-likelihood are employed, which allow optimizing a large number of hyperparemeters in a computational effective way. Based on experience from earlier emulation exercises [32; 33], the TOA radiance data was first compressed into 20 PCA components. The GPR emulator was trained with 70% of the 1000 samples and the remaining dataset was kept for validation. Goodness-of-fit statistics were calculated to assess the emulator's capability to generate accurate TOA radiance data: the Pearson's correlation coefficient (\(R^{2}\)) and root-mean-square error (RMSE) are calculated according to Equations (12) and (13):  
\[R^{2}=\frac{n\sum_{i=1}^{n}\left(X_{ref,i}\cdot X_{ret,i}\right)-\sum_{i=1}^{n} X_{ref,i}\cdot\sum_{i=1}^{n}X_{ret,i}}{\sqrt{\left[n\sum_{i=1}^{n}X_{ref,i}^{2}- \left(\sum_{i=1}^{n}X_{ref,i}\right)^{2}\right]\left[n\sum_{i=1}^{n}X_{ret,i}^{ 2}-\left(\sum_{i=1}^{n}X_{ret,i}\right)^{2}\right]}}, \tag{12}\]  
and:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(X_{ref,i}-X_{ret,i}\right)^{2}}, \tag{13}\]  
where \(X_{ref,i}\) and \(X_{ret,i}\) are respectively the reference and retrieved values. Finally, the trained GPR emulator was imported into the GSA toolbox. In the GSA toolbox, the number of emulations executed was (N(k+2), where N represents the number of samples and k the number of input variables. We chose 1000 runnings per variables. This led to 14,000 runnings to compute the GSA sensitivity indices. The GSA results provide insights into the role of the driving variables at TOA as observed by a satellite sensor. Based on these insights, hybrid retrieval schemes were developed for retrievable biophysical variables, as described below.
## 7 Conclusions and Summary  
In conclusion, the quality of the GPM IMERG precipitation product at half-hour and 0.1\({}^{\circ}\) resolution was evaluated, using daily precipitation observation data from over 2800 rain gauges across Mexico as reference. The STEPS model was applied to explore the potential of the GPM IMERG precipitation product for real-time short-time precipitation nowcasting (forecasting) in this analysis. The analysis was divided into two parts: the first part used rain gauge data as a reference for data quality evaluation, while the second part evaluated the GPM IMERG data. Five events were selected, and the data was applied in the STEPS model to present the nowcasting performance over a relatively short lead time period of 0-6 h. The main conclusions of this study are as follows.  
(1) The quality of the GPM IMERG precipitation product at annual and national scales was investigated. The performance of the data was represented by several evaluation metrics, which were RMSE, CC, MAE, BIAS, POD, FAR and CSI. It was found that the GPM IMERG precipitation product showed a slight underestimation over Mexico, with small BIAS evaluations of \(-7.37\%\) and \(-6.87\%\) for the annual scale and national scale, respectively, and that fluctuations between over and underestimating at the daily scale were due to precipitation intensity and latitude. The quality of GPM IMERG data improved at the national scale, with the lowest estimated error.  
(2) The GPM IMERG product is able to reproduce a mostly consistent distribution of precipitation at the annual scale. The overall results show the GPM precipitation data in high agreement with the data from the rain gauges and provides better precipitation observations in remote areas, with great CC and POD values.  
(3) The nowcasting model was applied to explore the potential of GPM precipitation data for precipitation real-time short-term nowcasting and forecasting. The performance of precipitation nowcasting was examined by the RMSE, CC, BIAS and MAE metrics. The  
Figure 13: Box plot diagrams of ensemble precipitation nowcast skill as a function of lead time for averaged (**a**) root mean square error (**b**) mean absolute error; (**c**) correlation coefficient; and (**d**) BIAS metrics.  
errors between the STEPS nowcasts and GPM observed precipitation were estimated and plotted. Results from a comparison of each event showed qualitatively similar trends. The accuracy and quality of precipitation nowcasting generally decreased as the lead time increased, and the nowcasting quality reduced rapidly when the lead time reached 120 min (CC < 0.5). Therefore, the precipitation nowcasting performance by using GPM data is acceptable in Mexico beyond 2 h, after which the errors in the forecasts tend to increase very rapidly.
## 3 Engineering Example
### Numerical Analysis Based on the Response Displacement Method
#### 3.2.1 Calculation Model  
(1) Lateral Model of Immersed Tunnel  
In order to study the stability of each wall of the immersed tunnel under earthquake conditions, the beam element (BEAM188) is used to simulate its walls, while the spring element (COMBIN14) is adopted to simulate the effect of surrounding soil on the tunnel's structure. Based on this, the lateral seismic response model is established, as shown in Figure 9.  
(2) Longitudinal Analysis Model of Immersed Tunnel  
To investigate the deformation patterns of joints in the immersed tunnel under seismic conditions, 46 segments of the immersed tube in a representative sea area are selected for analysis, with a total length of 2070 m. The beam element (BEAM188) is chosen to simulate the immersed tunnel joints, while the spring element (COMBIN14) is used to simulate the rotating spring, axial spring, and shear spring, respectively, of the joints. Moreover, COMBIN14 is used to convey the forced displacement effect of the deformation in the foundation strata on the tunnel under earthquake conditions. The beam-spring model system formed is shown in Figure 10.  
Figure 8: Time-history curve of seismic wave: (**a**) X-direction seismic wave and frequency spectrum; (**b**) Y-direction seismic wave and frequency spectrum; (**c**) Z-direction seismic wave and frequency spectrum.  
Figure 9: Transverse beam–spring model.  
(3) Free-Field Analysis Model  
The nonlinear material properties of soil are crucial in dynamic analysis. Consequently, a two-dimensional free-field model was established using the SuperFLUSH/2D program for equivalent linear analysis. The finite element model, depicted in Figure 11, incorporates viscous boundaries at the base and sides. Ground motion is introduced as an equivalent nodal force, enabling the determination of relative soil layer displacement along the immersed tunnel's entire domain through nonlinear seismic response analysis.  
Relative displacement values of the surface, as well as the top and bottom of immersed tunnels under various ground motion conditions, are extracted and shown in Figure 12. These displacements are then applied to the ends of soil springs in the corresponding soil layers of the longitudinal beam-spring model. This approach allows for further analysis of the opening and relative angle of the immersed tunnel joints.  
Figure 11: Longitudinal finite element model of the entire domain of immersed tunnel.  
Figure 10: Longitudinal beam–spring model.
## Materials and Methods
### Characterization of Thickness Variation and Spring Effect  
All samples were cut from the center into two equal pieces using a C350 diamond blade cutter with a 350 mm diameter. Inward-facing cross-sectional areas of all samples were then polished by 500, 800, and 1000 grit sandpapers, respectively. To quantify the laminate thickness, nine measurement locations were selected, with three located in the left flange, three in the corner, and three in the right flange, as shown in Figure 4. The thickness at each individual location was then measured using a Mitutoyo 0-25 mm micrometer screw gauge with a precision up to 0.001 mm. For each location, an average of six readings were calculated. The coefficient of variation (CoV) of the thickness, shown in Equation (1), was used to compute a single numerical metric for the thickness non-uniformity of the entire laminate (\(n\) is the number of measurement locations). Once the samples completed their curing cycles in the oven, the mold was allowed to cool down for 20 min. The samples were removed from the mold and then they were allowed to spring back or spring forward. The angle of deformation was measured for every specimen. The coefficient of determination (R\({}^{2}\)) was calculated to quantify the spring effect, which is a statistical measure of how close the specimens were to the actual mold shape.  
Figure 4: Schematics of (**a**) slicing of composite laminate from the center in a longitudinal direction and (**b**) cross-sectional view of composite laminates with thickness measurement locations. The actual specimens slicing and thickness measurement criteria are uniform in both (**c**) concave- and (**d**) convex-cornered composite laminates.
## 5 Experimental Validation
### Comparative Experiments on Low-Speed  
To verify the low-speed performance of the control method, the system is decelerated from an initial speed of 100 r/min to 50 r/min, and then further decelerated to 20 r/min. The results of the low-speed comparison experiment are shown in Figure 7.  
From Figure 7a, the new control method exhibits a chattering amplitude of around 3 r/min under low-speed conditions, significantly lower than the chattering amplitude of around 6 r/min seen with the traditional DCC method. From Figure 7b, the settling time of  
Figure 7: Comparative experimental results of low-speed operation. (**a**) Comparative curve of speed response. (**b**) Comparative curve of speed tracking error. (**c**) Comparative curve of speed synchronization error.  
the new control method under low-speed conditions is around 2.5 s, while the settling time of the traditional DCC method is around 5 s. From Figure 7c, the new control method has a significant advantage in synchronous error of around 3 r/min, while the synchronous error of the traditional DCC method is around 7 r/min. Therefore, the new control method can still maintain good responsiveness and tracking performance under low-speed conditions, and effectively reduce the chattering phenomenon of the system.
# Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network
## 4 Experimental Results and Discussion
### Discussion  
Aiming at the prediction of underwater acoustic transmission loss, we proposed a new method for the prediction of underwater acoustic transmission loss. Under ideal marine environment and constraint conditions, we constructed sample data to train and test the accuracy of the new method and finally verified by sea trial transmission loss data. Under the ideal marine environment and constraint conditions, the parameter changes are controllable, the model applicable parameter boundaries are clear, and simulation testing is easy. In the simulation test, the adaptation accuracy of the underwater acoustic transmission loss model in the new method reached 94.86%, indicating that the recognition rate is already very high. Under constrained conditions, the sample dataset generated based on the normal distribution conforms to the actual marine environment parameter distribution, which is conducive to training stable and reliable results.  
Compared with the ideal environment condition where the characteristic parameter boundary during simulation training is clear, the characteristic parameter of the sea test environment has a certain degree of complexity, and the boundary overlaps each other, which increases the difficulty in adapting the acoustic model. Without in-depth research on  
Figure 12: Comparison of absolute error between system prediction and classic model prediction.
# Determinants of Y-Shaped Agility Test in Basketball Players
###### Abstract  
Y-shaped agility test is a reactive agility test that includes both sensory and motor components. However, there is a lack of information about the contribution of these components to reactive agility in basketball players. Therefore, this study investigates: (i) the relationship between Y-shaped agility performance and reaction speed, sprint speed, change of direction speed, muscle strength, and (ii) determinants of this reactive agility test. Eleven male basketball players performed a Y-shaped agility test, 5 m and 20 m sprints, a 505 Agility test, squat, countermovement and drop jumps, and simple and two-choice reaction time tests. The results revealed a significant relationship between the time in Y-shaped agility test and 5 m sprint time (r = 0.795, \(p\) = 0.003, \(R^{2}\) = 0.632), 20 m sprint time (r = 0.676, \(p\) = 0.022, \(R^{2}\) = 0.457), and reactive strength index (r = \(-\)0.619, \(p\) = 0.042, \(R^{2}\) = 0.383), but not with simple and choice-based reaction times. These findings indicate that performance in the Y-shaped agility test is more profoundly determined by a motor rather than a sensory component. This may be ascribed to the structure of a predominantly velocity-oriented Y-shaped agility test with a small contribution of responses to visual stimuli. The adaptation of basketball players to training aimed at increasing speed and strength may also play a role. The structure of the Y-shaped agility test should be modified so as to better reflect the sensory component of reactive agility and more sensitively reveal within- and between-group differences.  
change of direction; motor component; reactive agility; reactive strength; sensory component; sprint speed +
Footnote †: journal: Medical Physics Letters
# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 4 Experiments and Results
### Implementation Details  
#### 4.2.1 Data Preparation  
For the iris dataset, different subsets of iris images need to be preprocessed with a uniform resolution of 224 \(\times\) 224, with all images being saved as 24-bit color, bmp format for post-processing. The specific steps are as follows:  
1. Use the open-source OSIRIS [(20)] to acquire the circle parameters of the original iris images.  
Figure 11: Example images of CASIA-Iris-Lamp (first line), CASIA-Iris-Syn (second line), CASIA-Iris-Interval (third line) and CASIA-Iris-Thousand (fourth line).  
2. Generate high-resolution images (HR images, 224 \(\times\) 224): use the bicubic interpolation method to resize the iris images to a radius of 105 pixels and subsequently crop images into a resolution of 224 \(\times\) 224 centered around the center of the pupil.
3. Generate low-resolution images (LR images, 56 \(\times\) 56): low-resolution images with a resolution of 56 \(\times\) 56 pixels are generated by bicubic interpolation downsampling of HR images with a resolution of 224 \(\times\) 224.
4. Save all images as 24-bit color, bmp format.  
In contrast to some super-resolution (SR) methods that employ overlapped patches as input and subsequently merge the output patches to form a finalized image, our model utilizes the entire low-resolution (LR) image as input to capture global information for iris images.  
In order to verify that the super-resolution technique is indeed helpful for low-resolution iris image recognition, the recognition performance of low-resolution images as well as super-resolution images under different methods should be compared in experiments. However, the size of LR images is different from the size of SR images, and iris recognition systems require the input iris images to have a consistent size and format for effective comparison and recognition. For comparison, we used the conventional interpolation algorithm bicubic to enlarge the LR size and test the recognition performance of the LR images.
## 3 Application to Reflectarray for Compact Antenna Test Range
### Antenna Definition, Near-Field Specifications and Unit Cell Characterization
On the other hand, Figure 4c shows the phase-shift response for oblique incidence with \((\theta=40^{\circ},\varphi=30^{\circ})\) at three frequencies belonging to the fifth generation new radio (5G NR) n257 band. In all cases, the phase-shift range is larger than 400\({}^{\circ}\), making it suitable also for multi-frequency optimization.
## 1 Introduction
[13] pointed out that hull deflection and elastic deformation of strut bearings should be taken into account in the process of shafting alignment of a high-speed naval craft. [PERSON] et al. [14] proposed a four-degree-of-freedom bearing support model to accurately describe the non-uniform dynamics behavior inside bearings, and investigated the effect of elastic bearing bush deformation on shafting alignment utilizing this model. The influence of elastic deformation on marine shafting alignment is presently a focal point of research. However, the measurement methods for elastic deformation of supporting foundations of marine shafting remain underdeveloped in terms of comprehensiveness and operational efficiency. Emergent algorithms are being progressively applied to the control and optimization of shafting alignment [15; 16]. The integration of optimization algorithms with the BLINs is a prevalent method used for alignment optimization. Taking vertical bearing displacements as design variables, [PERSON] et al. [17] realized the comprehensive optimization of the alignment and vibration characteristics of a ship propulsion shafting, combining the BLINs with the genetic algorithm. [PERSON] et al. [18] used the quadratic programming algorithm and the BLINs to optimize the alignment state of the propulsion shafting of large ships. [PERSON] and [PERSON] [19] applied the multi-objective particle swarm optimization technique to optimize the vertical bearing positions of a marine propeller shaft system using the BLINs. Nevertheless, to the best of our knowledge, with regard to elastically supported shafting, there is still a notable lack of the identification and calculation method of the BLINs considering elastic deformation, posing significant challenges for the alignment analysis and optimization of this kind of shafting. In view of the issues mentioned above, a certain elastically supported submarine propulsion shafting was taken as the object, and the alignment optimization of this shafting was studied in this paper. The main contributions are as follows:  
1. An equivalent line-surface method was proposed, in order to effectively solve the problem of difficult measurement of elastic bearing displacement induced by elastic deformation of supporting foundations. 2. The influence of elastic deformation of supporting foundations on the BLINs matrix was analyzed, and the concept of the dynamic BLINs was proposed. In order to use the BLINs for alignment optimization, the response surface method based on GPR was used to establish the mapping relationship between the elastic bearing displacement and the dynamic BLINs. 3. Combining the response surfaces of the dynamic BLINs with the genetic algorithm, the alignment optimization of the shafting was realized. A new method has been developed for the analysis and optimization of the alignment of elastically supported marine propulsion shafting.