esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks
## 3 Novel Rate Adaptation Method
### The Goal and Assumptions of the Proposed Method  
The main goal of the developed method is to increase the efficiency of the available time-frequency resources usage, which is achieved by a local estimation and prediction of the radio channel parameters in the network node. It is assumed that the WBAN works in the TDMA mode without a slot contention stage. Time slots are organized into the fixed-length frames and useful data are generated with a constant or variable bitrate, and they are transmitted periodically. The transmission bitrate control is decentralized, thus, no coordinating node in the network is distinguished. Each pair of nodes establishes the transmission throughput with the Best Effort (BE) model without any traffic classification and priorities. However, the proposed method can be modified for the different data stream classes' usage. It is also assumed that nodes know a priori the power of signals transmitted by other nodes in the network. The transmitted power is understood as the power at the input of the transmitting antenna. Moreover, each node has two simultaneously operating RIs, i.e., the NB interface working at the 868 MHz frequency and the UWB one working at the 6489 MHz frequency. In the UWB interface, besides the main service, which is the data transmission, it is also possible to perform the radio distance measurements (RDMs) between network's nodes.  
The developed novel adaptive method for the data stream allocation (AMASD) assumes:  
* time synchronization of both RIs;
* realization of the RDMs via the UWB interface at the beginning of the time slot;
* the estimation of user motion parameters; and
* the determination of the LOS or NLOS conditions, basing on the impulse response of the UWB channel.  
The listed input data, due to the time synchronization of both RIs, are treated as coherent sources of information for both RIs. Information about the estimated channel parameters is obtained based on the acknowledgment packets in the NB interface and the RDMs packets for the UWB one.  
Based on the current research and the analysis described in Section 4.2.5, it was decided that the LQI parameter will not be used in the proposed method. It was also experimentally determined that the memory of the AMASD method should not exceed 100 transmissions due to its potential implementation on microcontrollers without high computing power and a data memory. Also, the method itself is designed to operate in real time.
## 3 Detailed Nonlinear Modeling of Grid-Connected VSCs during Asymmetric Faults
### Complete Nonlinear Mathematical Model of the System and Validation  
Combining the above mathematical model of the main circuit and control, the mathematical model of the system under asymmetric faults can be obtained, referred to as the full-order model in this paper, as shown in Figure 9.  
To validate the accuracy of the full-order mathematical model, a detailed electromagnetic transient switching model of the grid-connected VSC system, as shown in Figure 1, was constructed in PSCAD/EMTDC. The system parameters are listed in Appendix A, Table A1. Figure 10 compares the simulation results of the full-order mathematical model with those of the detailed switching model for Case 1.  
As can be seen from Figure 10, the transient waveforms of the full-order mathematical model and the detailed switching model are almost identical. This indicates that the full-order model constructed in this paper accurately reflects the transient behavior of the system during asymmetric faults and can be used for subsequent transient stability analysis.
## 3 Results and Discussion
### Surface Roughness
Figure 9: Vectorization of the microgroove network (**a**) for base surface and (**b**) after burnishing \(P=60\) N, \(f=1500\) mm/min (experimental run no. 9). The results show that \(P\) is statistically significant (\(p=0.0172\)) for \(Spc\). At the significance level \(\alpha=0.05\), the regression function was estimated for the presented parameters (Figure 10):  
* for Sa (7): \[S_{a}=-14.03+0.024465f+0.579667P-0.00093315 fP-0.00564667P^{2}+8.745\cdot 10^{-6} fp^{2}\] (7)
* for Sku (8): \[S_{\text{lsx}}=-87.1133+0.07872f+4.232P-0.0028856 fP-4.3067\cdot 10^{-7}f^{2}P-0. 051033P^{2}+0.000041 fp^{2}\] (8)
* for Spc (9): \[S_{pc}=63.883+0.0455f-0.0000574f^{2}-22.861P+0.001415 fP+0.192P^{2}\] (9)  
Figure 10: Plot of the regression functions of roughness parameters: (**a**) Sa, (**b**) Sku, (**c**) Spc. Table 9 presents the R\({}^{2}\) value and significance analysis (\(p\)-value) for individual structural elements of regression models for \(Sa,\;Sku,\) and \(Spc\) parameters. The R\({}^{2}\) value shows the high accuracy of the models. As in the previous analyzes, the results indicate that not all structural elements of the model are statistically significant. However, attempts to omit them in the model resulted in a decrease in the quality of the model--a decrease in the value of R\({}^{2}\).
## 3 Data
### Validation of the Computer Code for Data Generation  
New code was developed for the \(FS\) calculations in this study because existing computer programs cannot be easily coupled with deep learning or its associated data treatments for the purpose of comparing deep learning against LEMs. This newly developed code was validated against a widely adopted commercial LEM software package, Rocc Science, before its use. A trial dataset of five images per category was randomly chosen to evaluate the accuracy of the developed code. These slopes were then simulated using Slide2 in RocScience. Figure 3 compares the \(FS\) predictions obtained with RocScience and those obtained with the new computer code. In this figure, the lines represent the \(FS\) values obtained in this study, while the markers represent the \(FS\)s calculated with RocScience. As can be seen, the \(FS\) values calculated with this code almost coincided with the results obtained with RocScience for the four LEMs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Descriptions** & **Formulations** \\ \hline \((x_{1},y_{1})\) & Coordinates of Point 1 & \((0,15+10\lambda)\) \\ \((x_{2},y_{2})\) & Coordinates of Point 2 & \((15+10\lambda,y_{1})\) \\ \((x_{3},y_{3})\) & Coordinates of Point 3 & \((x_{2}+\lambda(29-x_{2}),y_{2}+8+\lambda(35-y_{2}))\) \\ \((x_{4},y_{4})\) & Coordinates of Point 4 & \((50,y_{3})\) \\ \(N\) & Number of slices & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coordinates of the four points used to create the geometry of the slope image data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soil Properties** & **Range** & **Unit** & **Color** \\ \hline Cohesion \((c^{\prime})\) & 10–100 & kPa & Red \\ Friction angle \((\varphi^{\prime})\) & 15–35 & Degrees & Blue \\ Unit weight \((\gamma)\) & 17–22 & kN/m\({}^{3}\) & Green \\ \hline \hline \end{tabular}
\end{table}
Table 3: Range of soil properties used in this study.
# Enhancing the Photocatalytic Activity of Immobilized TiO2 Using Laser-Micropatterned Surfaces
###### Abstract
In ISO 10676:2010, methylene blue assesses the photocatalytic activity of a surface; however, its photobleaching, influenced by photosensitization, may contribute significantly to the observed dye decomposition [47]. In ISO 10676:2010, DMSO is an unconventional test pollutant for photocatalysis, lacking recognition and extensive study compared to well-established pollutants like chlorophenols and pesticides. Furthermore, the use of this standard, requiring both ion and gas chromatography, adds complexity to its experimental implementation [47]. Consequently, the aforementioned ISO standards may not always be well-suited for their intended purposes. Conversely, the photocatalytic degradation of atrazine has undergone thorough investigation with various photocatalysts, rendering it a fitting substrate for appraising the photocatalytic efficacy of both bulk materials and surfaces. Moreover, its stability under UV light and low detection limit enable the photocatalytic assessment of surfaces with modest dimensions [48]. While microstructured silicon has been previously employed as a photocatalytic substrate with zinc oxide (ZnO) as the active material [49], the present study investigates the activity of the more commonly employed TiO\({}_{2}\) photocatalyst, combined with microstructured silicon substrates with increased surface area. The objectives of this study are (a) to develop and characterize novel thin TiO\({}_{2}\) films by the sol-gel process, (b) to spin-cast these films on laser-microstructured silicon substrates to form the photocatalytic surfaces, (c) to evaluate their photocatalytic activity against a selected organic pollutant (atrazine) in aqueous solutions under UV irradiation, and d) to investigate the stability of the photocatalytic surfaces through multiple cycles of usage. Furthermore, the photocatalytic activity of TiO\({}_{2}\) films on laser-microstructured silicon is evaluated against two other pollutants, i.e., 2,4,6-trichlorophenol and Bisphenol-a, to explore the application range of these surfaces. To the best of our knowledge, this is the first study that improves the photocatalytic efficiency of the traditional TiO\({}_{2}\) photocatalyst by combining it with novel patterned substrates. The resulting immobilized photocatalytic surfaces present the advantage that no filtration is required after water treatment to remove residues of the photocatalyst, which significantly reduces the cost of the process. All fabrication methods employed in this work (sol-gel synthesis and laser structuring) are amenable to scale up for the development of high-volume and low-cost photocatalytic reactors for water purification. This comprehensive investigation seeks to contribute valuable insights into the relationship between surface morphology and photocatalytic efficiency, offering a nuanced understanding that can inform the design and development of highly efficient TiO\({}_{2}\)-based photocatalysts for environmental remediation and energy applications.
# Spine, Pelvis and Hip Kinematics--Characterizing the Axial Plane in Healthy and Osteoarthritic Hips
## 1 Introduction  
The dynamic interaction between the femur, pelvis, and lumbar spine has been an emerging issue amongst hip and spine surgeons. The importance of this interaction was first highlighted by observational studies that reported inferior outcomes following hip arthroplasty in patients with spinal arthrodesis. Patients with spinal arthrodesis exhibit abnormal spinopelvic movement and have been shown to be at increased risk of dislocation following total hip replacement (THA) [1, 2]. Furthermore, observational radiographic studies have illustrated a wide variability in spinopelvic characteristics among THA patients [3].  
These observations [4, 5] emphasize the relevance of considering the spinopelvic characteristics in all arthroplasty patients, as they determine the position of the pelvis in space, which, in turn, determines the functional acetabular orientation. Acetabularcomponent orientation is an important determinant of hip function and has been shown to influence risk of wear [6; 7], instability [6; 8; 9], impingement [6], and patient-reported outcome measures [8]. Modelling studies have illustrated that the motion of the pelvis along each plane has a varying effect on acetabular component orientation [10]. Pelvic obliquity (motion about the frontal (coronal) plane) primarily affects cup inclination; pelvic tilt (motion along the sagittal plane) primarily alters version; and pelvic rotation (motion along the transverse plane) changes both version (mostly) and inclination [11].  
To date, all assessments have been performed in the frontal and sagittal planes as these two planes can be adequately studied with anteroposterior and lateral radiographs, respectively [4; 6; 12; 13]. However, very little information exists on what movements take place in the axial plane and how these may relate to the ones measured in the sagittal plane. Abnormal rotation movements of the pelvis about the transverse plane are likely to influence the outcome, as they have a significant effect on cup orientation and thus determine impingement risk. The study of axial plane movements during activities of daily living is not possible with radiographs. Thus, if these movements are to be best studied dynamically, a motion capture analysis may be the most appropriate setting. However, this has not been tested to date in arthroplasty patients. This prospective study aims to (1) examine the agreement between axial movement measured from the motion capture and the range of motion measured from sagittal radiographs of the spine, pelvis, and hip; and (2) determine the effect of hip osteoarthritis (OA) on the spine, pelvis, and hip kinematics by comparing asymptomatic volunteers with pre-THA patients suffering from unilateral or bilateral hip osteoarthritis (OA).
## 4 Results  
### Sentinel-1 Ice Velocity Map  
The S1 map of the surface velocity is displayed in north polar stereographic projection centered on Greenland, with an origin at 90\({}^{\circ}\)N, 45\({}^{\circ}\)W, a standard parallel of 70\({}^{\circ}\)N, and a reference to the WGS84 ellipsoid, corresponding to the projection used for the GIMP DEM [23]. Figure 1 shows the magnitude of the horizontal surface velocity. The velocity mosaic provides near complete coverage of the ice sheet areas west of the main ice divide. Some gaps exist in the interior eastern and southeastern sections of the ice sheet. The terminus sections of all outlet glaciers are covered. Whereas on outlet glaciers the matching signal for offset tracking is based primarily on amplitude features related to surface structure and roughness, distinct amplitude features are sparse in the interior of the ice sheet and stable speckle patterns are required for correlation of image chips. Preservation of speckle requires temporal coherence. In areas exposed to high snowfall and strong winds the temporal decorrelation of the phase signal limits the availability of suitable repeat pass pairs. Most of the gaps in the January to March, 2015 data set could be filled with additional S1 data acquisition during the following months.  
On some tracks, in particular in the northern section, stripes are evident being aligned approximately perpendicular to the satellite flight direction. These patterns are due to azimuth shifts induced by fluctuations in ionospheric electron density [29]. The ionosphere-induced noise is mainly of relevance for slow motion areas. It can be efficiently reduced by merging velocity data of multiple tracks.  
In order to illustrate and discuss particular features of the Sentinel-1 ice velocity product, we present detailed S1 velocity maps over some of the fast outlet glaciers in the following subsection and compare these with velocity maps derived from TerraSAR-X and PALSAR data.
# Abstract
## 4 Use Case Scenario
### Queries  
In the proposed design, the abstraction layer receives requests from the front-end application. If the requested data are not available in the persistence layer, the middleware queries the HE and external sources for the appropriate data. It is noteworthy that a single query from the front-end often results in a collection of different requests emanating from the middleware. After parsing, aggregating, processing and caching the data, the middleware responds back to the dashboard application providing the requested data in a JSON format.
## 3 Results and Discussion
### Relationship Between Spatial and Temporal Distribution of Sites and Environmental Factors
#### 3.2.3 Slope  
Using DEM data, the slope of Sichuan Province was extracted and classified according to the national Technical Regulations for Land Use Status Survey [36]. The site locations from different periods were overlaid with the slope classification to generate distribution maps (Figure 7). During the Stone Age, sites predominantly occurred in areas with slopes of 6-15\({}^{\circ}\) and \(>\)25\({}^{\circ}\), while regions with slopes below 2\({}^{\circ}\) had relatively few sites. A notable concentration of sites appeared within Aba Tibetan and Qiang Autonomous Prefecture, characterized by generally steep terrain. In the Shang to Spring and Autumn periods, sites were mainly distributed in areas with slopes of 2-6\({}^{\circ}\) and 6-15\({}^{\circ}\) within the Chengdu Plain, whereas in other slope ranges, they exhibited a more even distribution. During the Warring States period, the overall distribution of sites became more balanced, with the highest concentration observed in areas with slopes of 6-15\({}^{\circ}\). According to the distribution maps and statistical results, the majority of sites across all historical periods were located in areas with slopes below 15\({}^{\circ}\). The number of sites in this slope range peaked during the Shang to Spring and Autumn periods, mirroring the pattern observed in the relationship between site distribution and elevation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proximity to Water Sources** & **\textless{}1 km** & **1–3 km** & **\textgreater{}3 km** \\ \hline \multirow{2}{*}{Stone Age period} & Number & 241 & 154 & 81 \\  & Percent & 51\% & 32\% & 17\% \\ \multirow{2}{*}{Shang to Spring and Autumn periods} & Number & 243 & 84 & 20 \\  & Percent & 70\% & 24\% & 6\% \\ \multirow{2}{*}{Warring States period} & Number & 128 & 99 & 34 \\  & Percent & 49\% & 38\% & 13\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical table of distances from water sources for heritage sites across different periods.
## 4 Improved PSO-Optimized Fuzzy-BPNN Control Algorithm
### Improved Particle Swarm Algorithm
#### 4.2.2 Improved Particle Swarm Algorithm  
The inertia weight \(\omega\) has a balanced role in the global search for the optimal and development direction of the particle swarm algorithm and has an important impact on the performance of the algorithm. The adjustment of the inertia weight has a strong correlation with the number of iterations of the algorithm, and the inertia weight tends to decrement nonlinearly with the increase in the number of iterations; if the particles enter the optimal solution range during the iteration but the inertia coefficient does not reach the expected value due to the decrement, the particles deviate from the optimal. If the particle enters the optimal solution range during the iteration but the inertia coefficient does not reach the expected value due to decreasing, the particle deviates from the limit of the solution [31]. In order to make up for the shortcomings of the inertia weight parameter adjustment strategy, this paper improves the inertia weights of the particles with the expression:  
\[\omega=\omega_{min}-\frac{(\omega_{max}-\omega_{min})\times(f_{i}-f_{z})}{3( f_{a}-f_{z})} \tag{13}\]  
where \(\omega_{max}\) is the maximum value of the set inertia weight; \(\omega_{min}\) is the minimum value of the set inertia weight d; \(f_{i}\) is the current fitness value of the ith particle; \(f_{a}\) is the average fitness value of the population particles; and \(f_{z}\) is the optimal fitness value of the current particle.  
Equation (13) shows that the value of \(\omega\) is dynamically adjusted according to the difference between the particle runtime fitness value and the optimal fitness, thus improving the performance of the algorithm.
# Evaluating the Capture Efficiency of Microchannel Plates Through Photoelectron Detection
## 4 Conclusions  
In this study, we developed and tested a novel approach for evaluating the CE of some recently fabricated MCPs. By generating a stable photoelectron beam through UV light irradiation on a zinc plate and detecting the resulting electrons with MCPs, we obtained pulse signals corresponding to the electrons captured by the microchannels. Through statistical analysis of the intervals between adjacent pulses, we established that the average pulse interval serves as an indicator of CE, as it is determined by the number of electrons entering the MCP channels. Subsequent experiments with MCPs featuring different OARs confirmed a correlation between \(T_{\text{s}}\) and CE. Since structures with higher OARs capture more electrons, the tapered circular MCP demonstrated superior performance compared to the hexagonal MCP under identical conditions. This approach may provide a feasible alternative for assessing the performance of MCPs. Of course, further research is needed to fully elucidate the underlying mechanisms and refine the methodology.  
M.W.: data curation (equal); formal analysis (lead); investigation (equal); methodology (equal); writing--original draft (lead). L.Y.: data curation (equal); investigation (equal); methodology (equal). S.L.: data curation (equal); investigation (equal). X.Z.: resources (supporting). H.C.: funding acquisition (equal); resources (supporting). Q.Y.: conceptualization (lead); funding acquisition (equal); supervision (lead); writing--review and editing (lead). All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Key R&D Program of China (Grant No. 2022 YFF0709300).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restriction.  
**Conflicts of Interest**: The authors declare no conflicts of interest.  
Figure 6: (a) Schematic setup of the area-blocking experiment, (b) the graph depicts the relationship between measured pulse intervals and the OARs. Blue data points represent results derived from the area-blocking experiments, while red data points correspond to the actual measurements of MCPs with different open area ratios.
# Grain Sieve Loss Fuzzy Control System in Rice Combine Harvesters
## Material and Methods
### Cleaning Performance Evaluation under Different Working Parameters  
The sieve loss ratio and grain impurity ratio in the grain tank are two main factors that are used to judge the cleaning performance, and the higher those two values are, the worse the cleaning performance. Taking the fan speed, guide plate angle, and sieve opening as experimental factors, a cleaning experiment was carried out in the test bench (Figure 1) utilizing the threshed outputs from the thresh-separation test bench [18]. The total amount of threshed outputs was 60 kg, the feeding rate was 2.5 kg/s. An oil-skin was used to collect all the cleaning residual at the rear of the test bench; then, the full grains were filtered out from the MOG using the stationary re-cleaner (Agriculex ASC-3 Seed Cleaner, Guelph, Ontario, CA, Canada), weighed, and the grain sieve loss ratio was calculated. The grain impurity ratio can be calculated by sampling from the grain tank (0.2-6 kg with an accuracy of \(\pm\) 1 g) according to the national standard in China (DG/T 014-2009). The grain sieve loss should be \(\leq\)1%, and the grain impurity ratio should be \(\leq\)2% (Chinese standards JB/T 5117-2006). A preliminary screening experiment was designed with D-optimal design criterion, which focuses on precise parameter estimation, and the experiment results indicated that the fan speed, guide plate II angle, and sieve opening are the main factors that affect the cleaning performance. In this paper, a response surface experiment was designed with I-optimal design criterion in JMP12.0 software (SAS, Cary, NC, USA) to learn the variation trends in the cleaning performance [19]. The basic characteristics of the rice used in the test are shown in Table 2.  
Figure 4: Hardware structure of operating monitoring and control system.