esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis of Benzoxazine-Based N-Doped Mesoporous Carbons as High-Performance Electrode Materials
###### Abstract  
In this work, nitrogen-doped carbon materials (NCMs) were prepared using aniline-phenol benzoxazine (BOZ) or aniline-cardanol benzoxazine as the carbon precursor and SBA-15 as the hard template. The effects of the carbonization temperature (700, 800, and 900 \({}^{\circ}\)C) and different nitrogen contents on the electrochemical properties of carbon materials were investigated. The samples synthesized using aniline-phenol benzoxazine as precursors and treated at 900 \({}^{\circ}\)C (NCM-900) exhibited an excellent electrochemical performance. The specific capacitance was 460 F/g at a current density of 0.25 A/g and the cycle stability was excellent (96.1% retention rate of the initial capacitance after 2000 cycles) in a 0.5 M H\({}_{2}\)SO\({}_{4}\) electrolyte with a three-electrode system. Furthermore, NCM-900 also exhibited a high specific capacitance, comparable energy/power densities, and excellent cycling stability using a symmetrical electrode system. The characterization of the morphology and structure of the materials suggests it possessed an ordered mesoporous structure and a large specific surface area. NCM-900 could thus be considered a promising electrode material for supercapacitors.  
benzoxazine; nitrogen-doped mesoporous carbon; electrode material; electrochemical performance; supercapacitor +
Footnote †: journal: Applied Physics Letters
# Prediction of Cardiorespiratory Fitness Level of Young Healthy Women Using Non-Exercise Variables
The Minnesota Leisure-Time Physical Activity Questionnaire (MLTPAQ) focuses on collecting information on leisure-time physical activity, which is a very important component of total physical activity [22], significantly influencing cardiovascular risk factors in the adult population [23; 24]. [PERSON] et al. [25] conducted research aimed at, among other things, estimating the norms of CRF values in the Czech population using the non-exercise equation, including physical activity level, smoking status, age, BMI, waist circumference, and resting heart rate. The CRF values in MET units were presented. This method of estimating CRF was also used in a study by [PERSON] et al. [26]. In the case of the studies carried out by [PERSON] et al. [27], the predictive factors for estimating VO2 max were physical activity variables, smoking status, age, gender, and BMI. Similar variables were used in their research by [PERSON] et al. [28]. The authors compiled non-exercise variables with direct CRF estimation methods. They selected distinct non-exercise prediction equations, i.e., BMI, waist circumference, dyslipidemia, and hypertension. Participants also completed a questionnaire about health history using the BALL ST questionnaire. To predict maximum VO2 max values in healthy individuals, [PERSON] and [PERSON] [29] developed non-exercise models using various machine learning methods. The models included variables such as age, gender, BMI, body fat, lean body mass, and the activity code (AC) questionnaire variable describing the level of physical activity. The predicted VO2 max, measured during a 3-min walk, was determined based on age, sex, and body composition by [PERSON] et al. [30]. The literature review presented here demonstrates the important role of predictive models in the estimation of maximal oxygen ceiling as an indicator of health. Most of the models presented were based on either maximal or submaximal tests. The use of non-exercise models, which by definition are simpler models to use, provides new opportunities for health monitoring. To the best of the authors' knowledge, no models have yet been determined for the female student population based on anthropometry parameters and the level of declared physical activity. Additionally, this is the first study on such a large group of female students (N = 154) using direct measurement of VO2 max. The main objective of this study was to calculate predictive models of cardiorespiratory fitness for healthy and young women using the non-exercise variables. The calculated model was based on age, anthropometric parameters, and level of leisure-time physical activity.
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 8 Performance Evaluation
### Experiment with Fault Detection Methods  
We measured the performance of our method and previous approaches spanning various noise levels, with results listed in Table 8 and presented in Figure 10. XFI, CS, and CRFS are based on a one-time analysis during offline learning that defines the selected features. CRFS maintains smaller packet sizes compared to other methods that require all features to be transmitted before they can be processed, so they have a maximum packet size of 343.78 KB. Another advantage of CRFS over XFI and CS is that defining the number of selected features is not required because this is set as an objective. CS+LDA and XFI+DNN have another drawback in that they must generate statistical features on every sample, requiring more time and computational resources. CRFS uses the raw feature subset rather than statistical features, thus CRFS can define the sensor to send only those selected features, while XFI and CS still need to retrieve all of the data to be converted into statistical features before filtering. Due to the model simplicity in CS+LDA as a traditional machine learning approach, this method could maintain the lowest detection time. XFI-XGBoost is also a lightweight model but suffers from the need to transform all features into principal components, which is time-consuming. A comparison of the complexity between the deep learning-based fault detection models is listed in Table 9 and suggests that the classifier part of our proposed LSTM-AE model maintains the least number of parameters and smallest model size. Because the LSTM-AE classifier features a lower complexity than the DNN and CNN architectures used in previous works, this approach also supports real-time predictions at the edge server. The CRFS+LSTM-AE obtains the second-lowest detection time. As the detection times of every algorithm are below 50 ms, this approach enables edge computing that is capable of real-time detection. In addition, the CRFS+LSTM-AE has the smallest packet size among these experiments. The accuracy of the CRFS+LSTM-AE also outperforms the others, even when processing data with a signal-to-noise ratio of 0 dB and above. The CNN also works well in noisy data, followed by the DNN, while XGBoost and LDA do not perform well.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 5 Outlook
Satellite data are important, especially for land-surface model development regarding soil moisture, total terrestrial water storage, evapotranspiration, streamflow, and land surface temperature, among other variables [55; 318]. An additional application concerns the investigation of surface sensible heat fluxes behind the mega-heatwaves that are projected to aggravate with the increase of subtropical droughts [319]; in this direction, satellite precipitation will ideally be used together with CDRs, like the recently-published Climate Hazards Center Infrared Temperature with Stations (CHIRTS) [320], and also with downscaled climate datasets that become available through open access [321]. In summary, satellite remote sensing of precipitation has the potential to considerably advance our understanding of the water cycle, and research has to be focused on answering the basic questions of the water cycle under climate change conditions, i.e., water vapor residence time in the atmosphere and recycling over the continents, precipitation more concentrated in extreme events, the increase of drought episodes in tropical and sub-tropical areas, and changes in the frequency and intensity of tropical cyclones [36]. The use of satellite data does not mean that ground-based data cease to be fundamental. However, recent surveys of available datasets show that the existence of no single satisfactory approach to the adjustment of gauge bias [322], poor spatial coverage, temporal inhomogeneity, and inadequate sharing of in situ observations are the key obstacles to obtaining more accurate estimates of terrestrial mean precipitation [323]. Keen attention must be paid to areas that are more affected by water scarcity, such as the African continent [324; 325], where water shortages may concern 37% of the 2025 population (~600 million people) and 57% in 2050 (~1.4 billion people), under the hypothesis of a decrease in water resources by 10%. Concerning water management, Biswas and Tortajada [326] point out unambiguously that \"countries will have to manage their water significantly better, because they will have no other choice\". Notably, data-scarce regions will need to rely on satellite data to help addressing such serious concerns. **Funding:** This research received no external funding. **Acknowledgments:** The authors are grateful to their home institution for allowing them to use time and facilities to complete the review. Two anonymous reviewers deserve gratitude for their helpful suggestions, which significantly contributed to improve the overall quality of the paper. **Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Our Proposed Novel Resource Allocation Scheme
### Optimizing Backhaul Resource Allocation Vector \(\mathbf{r}\)  
For a fixed user pairing and subcarrier mapping \(\underline{\mathbf{x}}\)\(F(\mathbf{x},\mathbf{r},\mathbf{\Phi}(\mathbf{r}))\) is concave in \(\mathbf{r}\) and constraint set Equation (14) is convex. The optimal backhaul resource allocation vector \(\mathbf{r}^{*}\) can be efficiently found by applying KKT conditions or CVX toolbox [30].
# A High Full Quality CMOS Image Sensor for Space Applications
## 2 Sensor Architecture
### Pixel Design for Modulation Transfer Function  
The MTF, which can be measured in vertical and horizontal directions, is degraded by crosstalk between adjacent PPDs. One of effective ways to minimize crosstalk is to maximize the distance between the neighboring PPDs. However, this requires smaller PPD area for a limited pixel size, resulting in smaller fill factor and quantum efficiency (QE). Figure 5a,b show top-down and cross-section views of the two adjacent pixels. In Figure 5a, the PPD, active devices, and in-pixel capacitor are placed to maximize the PPD area. The control signals are routed horizontally not to interfere with the incident light for high QE. In this design, the vertical MTF is much better than the horizontal MTF because the distance between the adjacent PPDs in the vertical direction is much larger than that in the horizontal direction due to the active devices and in-pixel MOS capacitor placed under the PPD.  
As shown in Figure 5b, the horizontal MTF is deteriorated by lateral light, and this cannot be improved by using STI. In order to improve the horizontal MTF without modifying the pixel design (especially the PPD size and shape, which were already optimized for optical and radiation performances), metal shielding [24] has been applied as shown in Figure 5c,d.  
In this work, the layout of PPD, active devices, and MOS capacitor cannot be changed due to consideration of other parameters and radiation damage effects. Figure 5c shows a proposed method of metal routing for enhancing MTF performance. The cross-section view in Figure 5d shows the mechanism for blocking the lateral light by the shielding metals.  
Figure 5: Layout of pixels and cross section view: **(a)** Proposed pixel layout including MOS capacitor without metal shielding; **(b)** Cross section view without metal shielding; **(c)** Proposed pixel layout with metal shield for enhancing MTF; **(d)** Cross section view with metal shielding.  
Since M1 was already used for the signal and power routings, as shown in Figure 5b,d, the space for shielding was insufficient. Even though the use of a stacked metal shielding (M2 and M3) helps to reduce the optical crosstalk, it also reduces the QE [29]. Therefore, only M2 was used for shielding in order to maintain the QE. The shielding metals can help to prevent generation of photo-charges near the PPD edges by blocking lateral light, so the horizontal MTF is improved. In addition, photo-charges that are generated in an active area can affect the pixel performance, so the active area is fully covered by metal layers to prevent the incident light. The measurement results show that the horizontal MTF has improved by three times from 0.131 to 0.402 with metal shielding.
## 3 Results  
The proposed algorithm was implemented in C++ and tested on a PC with an Intel i5-8300H CPU (2.30 GHz) and 8 GB RAM. The performance of the proposed algorithm was compared with the five representative superpixel generation algorithms, including SLIC [26], SNIC [27], ERS [22], LSC [32], and FH [19]. We used the implementations available online for all other methods. To ensure segmentation performance, all algorithms were run on the recommended parameters. The comparison experiment results on BSDS 500 [33] are shown in Figure 8. As a preprocessing step for image processing, the time cost was also an important indicator. In Figure 7(d), we also compared the efficiency in the average running time per image. Table 2 presents the mean and standard deviation performance metrics at \(k=400\). A visual comparison is displayed in Figure 9.  
Figure 6: The performance metrics of our approach with different \(\lambda\) values at \(k=400\).  
Figure 7: The performance metrics of our approach for different _Maxiter_ values.  
A good initialization is essential for superpixel algorithms. In Section 2.1, we propose a seed sampling strategy different from regular grid initialization, which introduces image entropy into seed sampling strategy to optimize the content sensitivity of the seed. Furthermore, the content initialization compares with regular initialization in the proposed method on the entire BSDS 500 dataset. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **BR** & **ASA** & **USE** & \
\begin{tabular}{c} **Computational** \\ **Complexity** \\ \end{tabular} &
\begin{tabular}{c} **Average Time** \\ **Per Image** \\ \end{tabular} \\ \hline SLIC [26] & \(0.863\pm 0.064\) & \(0.957\pm 0.021\) & \(0.042\pm 0.021\) & O(n) & \(0.076\) s \\ \hline SNIC [27] & \(0.865\pm 0.062\) & \(0.955\pm 0.022\) & \(0.044\pm 0.022\) & O(n) & \(0.070\) s \\ \hline ERS [22] & \(0.925\pm 0.040\) & \(0.954\pm 0.024\) & \(0.045\pm 0.024\) & O(n\({}^{2}\) log n) & \(0.442\) s \\ \hline LSC [32] & \(0.928\pm 0.040\) & \(0.960\pm 0.020\) & \(0.039\pm 0.020\) & O(n) & \(0.220\) s \\ \hline FH [19] & \(0.763\pm 0.102\) & \(0.921\pm 0.042\) & \(0.078\pm 0.042\) & O(n log n) & \(\mathbf{0.051\) s} \\ \hline Ours & \(\mathbf{0.931\pm 0.046}\) & \(\mathbf{0.962\pm 0.020}\) & \(\mathbf{0.038\pm 0.020}\) & O(n) & \(0.159\)s \\ \hline \hline \end{tabular} Bold indicates the best value of the corresponding metric.  
\end{table}
Table 2: Performance metrics of different superpixel segmentation algorithms at \(k=400\).  
Figure 8: Quantitative evaluation of different superpixel segmentation algorithms.  
results under the three metrics (i.e., USE, BR, and ASA) are reported in Figure 10, which shows that our sampling strategy is better than regular grid initialization.
## 4 Experimental Validation and Discussion
### GLCM-Based Texture Features of Sparse Time-Frequency Images  
To extract the GLCM-based texture features from sparse TFIs, the colored TFIs shown in Figure 10 should be transformed into gray images. In this study, the weighted average method was used to convert the color images into corresponding gray images, each of which had a pixel size of \(429\times 543\) and contained \(256\) gray levels ranging from \(0\) to \(255\). Moreover, given the characteristics of the TFIs shown above, the offset values \((\Delta x,\Delta y)\) were taken as \((1,0)\), \((1,1)\), \((0,1)\), and \((-1,1)\) corresponding to the four directions \(\theta=\{0^{\circ},45^{\circ},90^{\circ},135^{\circ}\}\), respectively. We then obtained the GLCMs with \(\theta=\{0^{\circ},45^{\circ},90^{\circ},135^{\circ}\}\), and thus, extracted the \(12\) texture features for each gray image.  
As mentioned above, \(80\) samples were chosen for Test 1. By extracting the GLCM-based texture features from each sample, we obtained values of the \(12\) features in Test 1, as shown in Figure 12. The samples were numbered from \(1\) to \(80\). No. \(1\) to No. \(20\) denoted normal (\(0.000\) in) samples, No. \(21\) to No. \(40\) denoted samples with a mild fault severity degree (\(0.007\) in) at the inner race, No. \(41\) to No. \(60\) denoted samples with a moderate fault severity degree (\(0.014\) in) at the inner race, and No. \(61\) to No. \(80\) denoted samples with a severe fault severity degree (\(0.021\) in) at the inner race.  
Figure 11: TFIs obtained using the STFA-PD from bearing vibration signals of different severity degrees: (**a**) 0.000 in, (**b**) 0.007 in, (**c**) 0.014 in, and (**d**) 0.021 in.  
The 12 texture features were found to have a relatively low discreteness for the same fault severity degree and good discrimination capability between different fault severity degrees. Therefore, the features could be used to conduct the classification of fault severity degrees of the bearings. From a discrimination viewpoint, among the 12 features, the energy features (T9, T10, T11, and T12) were the best, followed by the entropy features (T1, T2, T3, and T4). The contrast features (T5, T6, T7, and T8) performed the worst. The distribution range of each contrast feature of the samples was wide, and the distributions of the samples with different fault severity degrees overlapped with each other in various degrees.  
In addition, we found that, although a single feature had a certain degree of discrimination, it could not completely separate different fault severity degrees, given the observed overlap of sample distributions with different fault severity degrees. Consequently, it was necessary to employ multiple features for pattern recognition.
## 3 Research Method and Process  
### Numerical Model Construction and Experiment  
In this study, FLAC\({}^{\text{3D}}\) version 6.0 was selected as the numerical simulation software, mainly because of its powerful three-dimensional geological modeling and geotechnical mechanics simulation capabilities. FLAC\({}^{\text{3D}}\) (Fast Lagrangian Analysis of Continua in 3 Dimensions) adopts the explicit finite difference method, which can effectively simulate the progressive failure and instability process of geological materials and is especially suitable for simulating the surface deformation caused by coal mining subsidence. It shows good adaptability in the dynamic simulation of surface displacement and related geological response caused by coal mining subsidence and can accurately reveal the change law of slope morphology and its relationship with coal mining subsidence. In addition, FLAC\({}^{\text{3D}}\) supports dynamic adjustment of local material parameters and can flexibly simulate different engineering processes according to specific geological conditions and mining conditions. Its efficient three-dimensional modeling, flexible parameter modification and powerful post-processing function can extract displacement, stress, and strain data in real time and generate an intuitive and clear displacement field and stress distribution chart, which provides strong support for the study of the slope deformation law. As a widely recognized numerical simulation tool for geotechnical engineering at home and abroad, FLAC\({}^{\text{3D}}\) has achieved remarkable results in a number of studies on coal mining subsidence and surface deformation [32; 33]. However, the material model assumption of FLAC\({}^{\text{3D}}\) is relatively simplified, which may not fully reflect the complex nonlinear deformation behavior of a loess layer. Further experimental verification and model adjustment are needed to optimize the simulation results.
## 4 Case Study
### Function Deployment  
Each requested DL slice is modeled through a chain of seven learning functions that include DAF, DPrF, DLF, DPsF, DCF, GMUF and DMIF. A multi-layered integrated T/NTN infrastructure including vehicular, RSU, UAV, HAP, and satellite layers is considered. From a functional deployment perspective, Region 5, corresponding to Slice 5, is configured with Multilayer FL, a methodology designed to execute FL across distinct layers of edge devices. Specifically, end devices use their data sets to perform the learning process. The deployment involves the placement of DAF, DPrF, and DLF on a VU layer, facilitating the learning process. Subsequently, the acquired parameters are communicated to neighboring edge nodes, represented by RSUs, via DPsF to optimize communication costs. RSUs collect data from VUs and perform aggregation operations with the assistance of DCF and GMUF. The GMUF results are then transmitted to the upper layer, where the relevant functions reside. The deployment of the function for the third slice utilizing FD mirrors the FL process described. For Slice 1, employing SL, a data parallelization technique divides the learning process between the device and edge layers, implementing learning functions on both levels. In addition, the HAP layer is employed to formulate a global model. In the case of Slice 2, which employs GADMM, collaborative learning occurs across user devices, with appropriate functions located on the VUs cloud. The upper layers contribute to the creation of a generalized global model by aggregating local models. Slice 4, which caters to latency-critical applications, adopts a Transfer Learning-based DL approach, integrating past learning experiences into current cycles to mitigate learning process costs. The learning process is distributed across various edge layers.