esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Clinical Response
#### 3.1.2 LPSI and Manually Measured Epidermal Thicknesses  
The LPSI in the SP showed a significant reduction in the course of treatment (\(p<0.001\)) (see Figure 5) (for exact values, see Table 1).  
The presence of hyperkeratosis and acanthosis was observed in all patients before treatment (12/12), acanthosis was present at V1 in 9 patients (9/12), and hyperkeratosis in 9 of the 12 patients. At V2, acanthosis was still present in three patients (3/12), and hyperkeratosis, in three patients (3/12). Before treatment, the exact thickness of acanthosis was measurable in 4 of 12 patients (in the other patients, acanthosis exceeded the penetration depth of the LC-OCT device); at V1 and V2, acanthosis was measurable in 11 of 12 patients. The reduction in acanthosis during treatment showed statistical significance (\(p<0.001\)) (see Figure 6a). For one patient, acanthosis continued to exceed the device's penetration depth, thus remaining non-measurable.  
Exact hyperkeratosis was measurable at all times in all patients; its reduction was statistically significant (\(p=0.001\)) (for exact values, see Table 1).  
In the CoA, the LPSI was 0 for all patients at all study visits, and there was no sign for hyperkeratosis or acanthosis (for exact values, see Table 1). No statistically significant difference during the study period (see Figure 6b) could be observed.  
To assess whether the thickness of the SP converged toward that of the CoA, the difference between the SP and CoA was calculated at each time point and compared between visits. For ET as well as SCT measurements, this difference significantly decreased over the course of treatment (ET: \(p<0.001\); SCT \(p<0.001\); see Table 1 for exact values).  
Figure 5: (**a**) Reduction in LPSI score for all patients at each study visit (V0 before therapy, V1 after 4 weeks, V2 after 12 weeks of therapy). (**b**) First patient’s clinical pictures of SP on the left elbow before therapy (b-i, LPSI 6), after 4 weeks (b-ii, LPSI 2), and 12 weeks (b-iii, LPSI 2) after therapy; second patient’s clinical pictures of the SP left ventral thigh before therapy (b-iv, LPSI 9), after 4 weeks of therapy (b-v, LPSI 6), and after 12 weeks of therapy (b-vi, LPSI 1). LPSI = local psoriasis severity index. SP = study plaque. Thick horizontal line in the boxplot = median.
## 7 Conclusions  
In this study, we have analysed incremental cooperative communication for WBANs. Incremental cooperation for a single pair of the source and destination, with different numbers of cooperative relay nodes, is analytically studied. We proposed a three-stage incremental relaying scheme with three relay nodes. Analytical expressions are derived for the three-stage incremental relaying scheme and compared to the single- and two-stage relaying schemes. Simulation results show that the proposed three-stage relaying scheme gives high throughput with reduced PER in the presence of three redundant links for a single source node.  
Later on, the proposed three-stage incremental cooperation scheme is implemented in the network layer protocol designed for WBANs. Its performance is compared to the conventional cooperative protocol, Co-CESat, and the incremental cooperation protocol, InCo-CESat. Simulation results reveal that the less throughput and high packet error rate of Co-CESat is improved in EIn-CoCESat by utilizing three-stage incremental relaying. Co-CESat has a data redundancy problem at the sink due to conventional cooperation, which ultimately causes high energy consumption. Data redundancy at the sink is avoided by using incremental cooperation, so that relays forward the data only whenever it is needed. Hence, need-based incremental relaying helps to improve the data redundancy and data traffic load. The energy consumption of Co-CESat is also high because of redundant data transmissions. Therefore, by eliminating data redundancy, energy efficiency is increased by EIn-CoCESat. Furthermore, InCo-CESat has less throughput and high PER, which are improved by EIn-CoCESat by utilizing three-stage incremental relaying. Hence, analytical analysis and simulation results positively correlate with each other.
# Abstract
## 2 Materials and Methods
### Evaluations by Dentists  
For the empirical evaluation, 20 dentists (age: 24-40; 17 females, 3 males) in the Department of Fixed Prosthodontics, with no visual impairments and no experience with SRDs, were randomly selected from the Kanagawa Dental University Hospital. The subjects had a mean clinical dental practice of 2.45 years (SD: 3.15). Subjects had acquired adequate abutment formation skills, and uniformity in basic abutment formation techniques was maintained. The participants performed abutment teeth formation simulations for the upper-left first molar on both a regular 2D display and an SRD. The measurements were conducted in a random order, and prior to the measurements, the participants were asked to observe and familiarize themselves with the exemplar abutment tooth morphology. Subsequently, the training time was measured. Subsequently, each participant was given 5 min to create an abutment tooth, and the degrees of over- or under-formation and agreement with the exemplars were evaluated through an automatic evaluation function. The was an interval of 5 min between device changes. To avoid measurement bias, participants were not allowed to view the automatic assessment results. After the measurements, they were asked to fill in a feedback questionnaire. The feedback questionnaire asked the participants to answer 15 questions on a 5-point scale. The questions included four categories, covering the usefulness of this system, the usefulness of an SRD, learning effects, and physical impairments such as dizziness and fatigue. Subjects were fully informed about the study and signed written informed consent. This study was reviewed and approved by the Ethics Committee of Kanagawa Dental University (Approval No. 970). Figure 6 shows the flow of the actual equipment verification.  
Figure 6: Flow of verification of actual equipment. After obtaining consent, validation will be performed on both displays, followed by automatic scoring and a questionnaire survey.
# Modern Design of Carrier for Overhead Conveyor
## 2 Materials and Methods
### Optimization of Original Carrier  
One possible solution to this technical problem is to modify the original carrier. Thus, topological optimization was used for the modification. The optimization module in Ansys was used. An example of a setup directly for this software is given, for example, in [32]. Further examples and a theoretical analysis of the solver used in Ansys Workbench 2023  
Figure 4: Topology optimization process diagram [31].  
R2 from Ansys manufacturer from Canonsburg, PA, USA are given, for example, in the following articles [33; 34; 35; 36].  
For topological optimization purposes, the currently used carrier had to be remodeled in 3D from a drawing, which was the only documentation provided with the carrier. The original carrier in the form of a 3D model is shown in Figure 5. This original carrier was subjected to topological optimization to determine the areas where it would be best to remove material.  
The carrier was further modified for topological optimization. Elements that were not used in the analysis were removed due to their very low, or rather negligible, loading, but at the same time the necessity of using these elements. These are mainly the guide rods, which ideally are not loaded at all.  
This topological optimization is performed on the basis of a very important premise. If this optimization is advantageous, the existing carriers would be modified. This modification would consist of creating holes in the existing profiles. Modification by changing the thickness of the profile is very difficult or even unrealistic, or even very costly. For this reason, a model was used in which steel blanks and plates were used as planar components with an associated thickness. A representation of the model can be seen in Figure 6.  
In this geometry, the weight of the load is missing. This mass has been replaced by a mass point, which has been placed at the centre of gravity of the transported object. A  
Figure 5: Original carrier 3D model.  
Figure 6: Model of original carrier for Ansys.  
mass of 35 kg was considered. The representation of the mass point relative to the carrier is shown in Figure 7.  
The mesh and its size were chosen with respect to the needs of topological optimization. A coarse mesh could cause poor performance especially in topological optimization, a mesh that is too fine on the other hand greatly increases computation time. A representation of the model mesh for topological optimization is shown in Figure 8.  
The mesh element size was set to 10 mm. This size is sufficient for the needs of modal analysis and subsequent topological optimization. The model was solved as a combination of shell and solid elements. Parameters of the mesh see on Table 1.
# Accurate Constant Phase Elements Dedicated for Audio Signal Processing
## 2 Passive and Active Structures of CPE Approximants
Figure 1: Gallery of a fundamental passive circuit implementations of seventh-order approximations of constant phase elements (CPEs): (**a**) parallel-series RC ladder, (**b**) equivalent series-parallel RC structure, (**c**) series-parallel RL network, (**d**) equivalent parallel-series RL structure, (**e**) simplified transmission line for fractional-order (FO) admittance realization, (**f**) simplified transmission line for FO impedance realization, (**g**) FO two-port realization using first-order filtering sections, (**h**) FO two-port realization using second-order filtering sections, (**i**) active CPE realization using knowledge of zeroes and poles of the impedance function, (**j**) active CPE realization based on knowledge of significant frequencies and quality factors of impedance function, and (**k**) fragment of RC tree structure. In the Orcad Pspice circuit simulator, CPE two-ports can be emulated using a cascade of LAPLACE blocks. Both transfer functions from (12) can be, with appropriate numerical values of coefficients, written directly to a numerator and denominator and simulated via an AC sweep. Following this concept, we can study the influence of individual coefficients to the module and phase frequency response of CPEs, i.e., judge sensitivities. The quality of CPE approximation can be quantified with respect to many aspects, including user-defined figures of merit. However, the standard measure is the absolute error accumulated over the desired frequency range, i.e., by definite integral  
\[\varepsilon=\int_{f_{1}}^{f_{2}}abs\biggl{(}\sum\
olimits_{k=1}^{n}atan \frac{\omega}{\omega_{Zk}}-\sum\
olimits_{k=1}^{m}atan\frac{\omega}{\omega_{ Pk}}-\frac{\pi}{2}\alpha\biggr{)}df, \tag{15}\]  
where \(\omega_{Zk}\) and \(\omega_{Pk}\) are the positions of the \(k\)-th zero and pole, respectively, and \(\alpha\) is order of the CPE. Using this, sensitivities can be considered as a degree of displacement of individual zeroes and poles with respect to some circuit components.
###### Abstract  
Assessment and analysis of soil slope stability is an important part of geotechnical engineering at all times. This paper examines the assessment of soil slope stability in fine-grained soils. The effect of change in shear strength (\(\tau\)), shear stress (\(\sigma\)) and slope angle (\(\beta\)) on the factor of safety has been studied. It correlates shear strength with slope angle and shear stress by considering the horizontal seismic coefficients in both saturated and unsaturated conditions. The slope failure surface was considered a circular slip surface. Statistical package for social sciences (SPSS) and Slide, numerical modeling software and limit equilibrium slope stability analysis software, respectively, are used to find out the correlations between the three basic parameters. The slope angle varied from 70 to 88 degrees, which are the most critical values for slope angles, and a total of 200 analyses were performed. \(\tau\), \(\beta\) and \(\sigma\) are correlated, and the correlations are provided in the results section. The results indicate that the correlations developed between the parameters have a very close relationship. The applicability of the developed equations is above 99%. These correlations are applicable in any type of soil slope stability analysis, where the value of shear strength and factor of safety is required with the variation of slope angle and shear stress.  
slope stability analysis; shear strength; slope angle; shear stress; correlations +
Footnote †: journal: Applied Sciences
# Integration and Field Evaluation of an IoV System for Enhancing Road Safety
## 5 Results
### Technical Performance Results of the System
#### 5.1.2 Communication System Performance  
The system aims to address, as already mentioned, both equipped and non-equipped vehicles. The equipped vehicles have installed systems that implement V2X communications, while the non-equipped do not have any other type of equipment except a smart mobile phone to receive and display information intended for the end user. Since the used differences between the two approaches are large concerning the used telecommunication technologies, examples of the measured performance of the system are presented separately for each case.  
Figure 9 depicts the time between the creation of the awareness message (CAM) (which contains both the latest geographic position and the current motion parameters and feeds the applications) to the HMI notification. The non-equipped vehicle follows a different procedure which is measured based on the Figure 10 time intervals.  
The following graphs (Figures 11-14) present examples of the results regarding these time frames, as derived from reading taken during the pilot user trials of the system.  
Figure 11: Time delay between detection and application exit (\(\Delta\)t12 vs. ms).  
Figure 10: Sequence of messages in the system for the non-equipped vehicle.  
Figure 9: Time delay between CAM message and driver notification on the equipped HMI display in ms.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 4 Experimentation and Analysis
### Selection of the Main Parameters of the ECA-MRANet Model and Their Effects
#### 4.3.1 Selection of the Number of Residual CBAM Blocks  
Theoretically, deeper network structures usually lead to better expressive and learning capabilities, resulting in lower errors and higher accuracy on certain tasks. However, as the depth of the network increases, the complexity and parameters of the network also increase, making the training process more difficult. Therefore, in this paper, the number of Residual CBAM blocks stacked for each channel is investigated. Specifically, the number of Residual CBAM blocks is set to 2, 3, 4, and 5. To prevent the influence of uncertainties, the average of the results of 10 trials is counted for different numbers of Residual CBAM blocks of the network. Figure 12 illustrates the prediction accuracy and training time of the model under different numbers of Residual CBAM blocks. As can be seen from Figure 12, the prediction  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types of Bearing Faults** & **[a,K]** & **Optimal IMF Components** \\ \hline Inner ring crack & [1675,5] & IMF3 \\ Inner ring spalling & [1907,6] & IMF5 \\ Outer ring crack & [1884,6] & IMF5 \\ Outer ring spalling & [1560,4] & IMF1 \\ Rolling body spalling & [1984,3] & IMF2 \\ Healthy & [1532,3] & IMF2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimization results of VMD parameters for experimental data.  
Figure 11: Time–frequency diagrams for different fault types. (**a**) Inner ring crack; (**b**) Inner ring spalling; (**c**) Outer ring crack; (**d**) Outer ring spalling; (**e**) Rolling body spalling; and (**f**) Healthy.  
accuracy of the network increases with the number of Residual CBAM blocks. However, once the depth of the network has increased past a certain point, the recognition accuracy of the network increases only slowly, while the training time of the network increases substantially. Comparing the training time and prediction accuracy of the model under different numbers of Residual CBAM blocks, this paper opts to build the ECA-MRANet model with three Residual CBAM blocks.
## 2 Materials and Methods  
### Fine-Resolution SIF Dataset  
In this study, we mapped GPP globally directly from our recently produced fine-resolution SIF product (namely, GOSIF) [31] based on the linear SIF-GPP relationships that we established. The GOSIF data that we developed using a data-driven approach have much improved spatial (\(0.05^{\circ}\)) and temporal (8-day) resolutions compared with other gridded SIF datasets such as GOME-2 and GOSAT or gridded SIF data that are directly aggregated from individual OCO-2 soundings based on simple spatial averaging, and have continuous global coverage over the period 2000-2018. We used the Cubist regression tree model to create multivariate linear regression models between target variable (i.e., OCO-2 SIF from discrete soundings) and explanatory variables including EVI from MODIS and meteorological data (PAR, air temperature, and VPD) from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2). This model was trained based on a very large number of data points (\(\sim\)2.6 million) and therefore could encompass various climatic conditions and ecosystem types across the globe. The inclusion of meteorological data could largely account for the environmental conditions (e.g., water, temperature, solar radiation), which improved the performance in predicting SIF. Our model predicted SIF fairly well across various biomes, with a coefficient of determination (R\({}^{2}\)) = 0.79 and Root Mean Square Error (RMSE) = 0.07 W m\({}^{-2}\)\(\mu\)m\({}^{-1}\) sr\({}^{-1}\). The derived GOSIF was highly correlated with tower GPP for all major biomes, with R\({}^{2}\) ranging from 0.52 to 0.85 (p \(<\) 0.001). More details on the GOSIF product are described in [PERSON] and [PERSON] [31].
## 4 Simulation Setup and Parameters
### Performance Metrics  
System performance is assessed using several key metrics: Root Mean Squared Error (RMSE) between predicted and true AoAs, average SNR improvement compared to non-predictive beamforming, and the percentage of slots where beam selection aligns with the optimal direction. The system also tracks throughput per user, calculates it using the [PERSON] capacity formula, and analyses latency at each layer (O-RU, O-DU, and fallback MUSIC). The robustness of prediction and fallback is stress-tested by introducing trajectory curvature, Doppler, and substantial interference, as well as by monitoring the frequency of fallback events. Results are visualised using plots of AoA error, SNR over time, and spatial heatmaps, providing a comprehensive assessment of system adaptability, responsiveness, and practical O-RAN integration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Range** \\ \hline Carrier Frequency (fc) & 5.9 GHz \\ Number of Antenna Elements (N) & 8, 16, 32 \\ Antenna Height & 20 m \\ Number of Users (U) & 1–8 \\ User Height (fixed) & 1.5 m \\ Antenna Spacing (d) & \(\lambda/2\) \\ Snapshots (T) & 20, 100, 1000 (per scenario) \\ Time Interval (t) & 1 ms–160 ms \\ User Initial Distance & 10 m \\ User Distance & 150–200 m \\ User Speed & 30 km/h and 50 km/h \\ SNR Levels & 0–30 dB \\ Number of Multipath (M) & 3 per user \\ Window Size & 5 (with tests at 10 and 20) \\ AWGN Power & 8.004 \(\times\) 10\({}^{-13}\) W (calculated for TH\_noise = 290 K, \\ B = 20 MHz) \\ Rayleigh Fading & Variance = 0.8, Mean = 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.
## 4 Results
### Temporal and Spatial Changes
#### 4.1.4 Remote-Sensing Ecological Index  
In order to quantify the environmental benefits brought by the \"Kokyar Greening Project\" to the region over the past 22 years, this study selected four important indicators closely related to the land-surface ecological environment, i.e., greenness, humidity, heat, and dryness. The remote-sensing ecological index (RSEI) data of the Kokyar region for the past 22 years were calculated.  
The spatial change trend of the RSEI in the Kokyar Project Area was consistent with that in the project construction phase (Figure 1). In 2000, the high-RSEI areas were basically in the middle and east of the project area, i.e., the construction areas of the Phase A, B, and C projects, and the north and south of the project area were low-RSEI areas. Over the past 22 years, after the construction of the Phase D, the low-value areas of the RSEI in the north and south of the project area have been significantly improved, which is mainly reflected in the gradual reduction in the area of the low-value areas of the RSEI and the significant increase in the medium- and high-value areas in the north and south (Figure 5a). In 2000, the area above the good level of the RSEI (>0.6) was 44.86 km\({}^{2}\), accounting for about 39% of the total area of the project area. In 2021, the area above the level of the good level was 66.04 km\({}^{2}\), accounting for about 58% of the total area of the project area. In 2021, the area above the good level of the RSEI increased by 21.18 km\({}^{2}\) compared with that in 2000, and the effect of desertification control was remarkable.  
As shown in Figure 5b, from 2000 to 2021, the mean value of the RSEI in the project area showed a significant upward trend on an interannual scale, with an average increase of 0.02 every 10 years. The multi-year average RSEI was 0.53, indicating that the total ecological quality in the project area was at a medium, close-to-good level. In 2020, the RSEI reached the highest value, 0.62, and the RSEI was at a good level, with an increase of 41% compared with that in 2000 (0.44). It shows that the \"Kokyar Greening Project\" has greatly improved the ecological quality of the area over the past 22 years.  
Figure 4: Spatiotemporal changes in VEQI in Kökyar Project Area from 2000 to 2021. (**a**) Spatial distribution map of FVC in Kökyar Project Area from 2000 to 2021. (**b**) Time-series changes in VEQI in the Kökyar Project Area from 2000 to 2021.