esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Detection of Renal Failure  
An HRV analysis is a valuable tool for assessing autonomic dysfunction, which is vital in predicting the cardiovascular morbidity and mortality associated with kidney failure patients [156]. HRV indices have been analyzed [157] by researchers on individuals diagnosed with renal failure. The association between HRV features and the concentrations of electrolyte ions before and after dialysis was presented in a paper by [PERSON] et al. (2002) [158]. The HRV features from 5 min ECG signals of twenty patients with chronic kidney failure (CRF) were evaluated. After hemodialysis, the study showed calcium negatively correlated with the average RR intervals and the normalized HF power. [PERSON] et al. (2019) investigated the diagnostic importance of HRV on renal function in kidney failure patients [156]. The authors involved 326 non-dialysis chronic kidney disease (CKD) patients in experimental research of 2.02 years, and their HRV features were analyzed regularly. It was found that the values of HRV features became reduced with the increased severity of CKD. The computation of LF/HF divulged useful information on the progress of CKD apart from the other risk factors. Some more recent articles on the application of HRV features in renal failure are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Author, Year** & **Feature Extraction Methods Used** & \begin{tabular}{c} **Feature** \\ **Selection/Reduction** \\ **Methods Employed** \\ \end{tabular} & **Classifiers Used** & **Inference** \\ \hline [PERSON] et al. & Time-domain, & \begin{tabular}{c} Mann–Whitney U \\ test, Wilcoxon \\ rank test \\ \end{tabular} & \begin{tabular}{c} Multivariate \\ regression analysis \\ \end{tabular} & \begin{tabular}{c} The study revealed that HRV could be \\ used as a marker of atrial fibrillation \\ (AF). In contrast to paroxysmal AF, HRV \\ was higher in persistent AF. It indicated \\ a clear autonomic role in the \\ pathophysiology of chronic AF. \\ \end{tabular} \\ \hline [PERSON] et al. (2018) [153] & Time-domain and & \begin{tabular}{c} Pooling methods, \\ frequency-domain \\ methods, entropy \\ features \\ \end{tabular} & \
\begin{tabular}{c} L1-regularized \\ namely average, \\ maximum, minimum, \\ and magnitude \\ \end{tabular} &
\begin{tabular}{c} The study proposed a method that can \\ distinguish hypertensive patients from \\ healthy controls with 93.33\% accuracy \\ when tested on 24 hypertensive patients \\ and 24 healthy controls. \\ \end{tabular} \\ \hline [PERSON] & & & & The study indicated that hypertensive \\ [154] & Non-linear methods & - & - & SD1 (calculated using Poincaré plot) \\ \hline [PERSON] et al. (2019) [155] & Time-domain, & & & The study proposed an SVM classifier \\ (2019) [155] & and non-linear methods & - & SVM, PNN, and \\  & & & KNN \\  & methods & & & \\ \hline \end{tabular}
\end{table}
Table 3: List of published papers in recent years that have used HRV analysis methods for the detection of hypertension.
# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 5 Discussion
### The Height Divider of PL Obtained from the Height Histogram  
To calculate the height divider, 602 grids were selected in a span (in red box, see in Figure 4a) without tower points. Figure 21a shows the height distribution histogram of the points, which conformed to the Gaussian mixture model [29]. The curves drastically fluctuated from 16 to 20 m, 22 to 25 m, 28 to 31 m, and 37 to 39 m, which was the height range of PL points. The first peak, between 16 and 20 m, was considered to be the initial distance between the transmission line and other ground objects.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Grid Size** \\ **(m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Grid** \\ **Sampling** \\ **Interval (m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Total PL** \\ **Sags** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Correct** \\ **Extraction** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Erroneous** \\ **Extraction** \\ \end{tabular} } & \multirow{2}{*}{**Residual**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{
\begin{tabular}{c} **Time-Consumption** \\ **(s)** \\ \end{tabular} } \\ \hline
1 & 0.223 & 50 & 32 & 14 & 4 & 64\% & 14.114 \\
2 & 0.188 & 50 & 38 & 8 & 4 & 76\% & 9.556 \\
3 & 0.165 & 50 & 44 & 6 & 0 & 88\% & 7.866 \\
4 & 0.146 & 50 & 47 & 2 & 1 & 94\% & 7.057 \\
5 & 0.13  
As depicted in Figure 21b, the strokes in the dotted eclipse indicates the density of points distributed along the vertical direction. The top waved curve refers to the density of PL points, which evidently peaked four times along the vertical axis. Thus, it was rather easy to tell the difference between PL and other ground points using this diagram. In the data, the HV PLs were mostly distributed around a height interval of 30-60 meters, as the adjacent towers were usually built upon hilltops. In fact, they are required to be more than 8 meters above the ground in the power corridor, according to the regulations for the heights of PLs in China's _Operation code for overhead transmission line_. The height divider, as shown in Figure 21c, was set as the starting height for PL segmentation. The division can be seen in Figure 21d. If there was a slope in the span, the divider was less obvious  
Figure 21: Subdivision analysis of the power corridor data: (**a**) height histogram of the point cloud where the vertical axis is the number of points; (**b**) diagram of PL point distribution and the gap in vertical distribution; (**c**) a point cloud fragment containing PLs; (**d**) height histogram of points in the data within two spans.  
and its value needed to be readjusted. Thus, it was necessary to comprehensively consider the influence of topographic relief on the height histogram.
## 5 An SOA-Based Platform Example and Case Study Results
### Results from the Five Cases  
Visualization with Summary of ImprovementsThe results from the case study and its five cases, plus a summary of the improvements perceived, are visualized in Table 2 in order to provide a comprehensive overview. Additional details on the results are available in the following subsections. Within Table 2, an \"x\" means the case finds significant improvements during that phase of the lifecycle. Further, an \"o\" is the same as an \"x\" but multiple use cases can be part of this entry.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Author** & **Challenge** & **Opportunity** & **Supporting Pillars** & **Comments** \\ \hline \multirow{6}{*}{[PERSON] et al., 2021 [7]} & Improve operational efficiency in automation systems and the related business business processes & Improved operational efficiency in business processes through robotic process automation & Use of robotic process automation, classification algorithms, and AI in cloud-based cyber-physical systems within a robotic mobile fulfillment system & Data-driven order correlation and storage allocation assignment problems are solved by improved classification algorithms used for intelligent automation \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._Thus, the use of the SOA-based platforms varies somewhat among the cases, depending on the focus and targeted problems. However, using pattern matching [30], it is clear that the companies who develop solutions/products, etc., find that there is a clear and significant benefit during the following phases: business development, design/development/piloting/test, installation/commissioning, and operations. LKAB, a mining company, sees the main benefits at the start and end of the lifecycle, where LKAB also gets the most value due to the fact that the length of the operations phase is commonly the longest phase of the lifecycle. Arrowhead Tools, which is a different being compared to the other case organizations, places more focus on the engineering phases of the lifecycle, as well as on the following operations phase. All cases except LKAB, which has yet not measured any hard data concerning improvements, indicates that there is an overall minimum improvement of approximately 50% and, on the upper side, a change in magnitude. In addition, although without any hard data to support the views, LKAB states that \"we do not want to go back again to 1-to-1 integrations!\" which supports the contention that there is a clear benefit/improvement from using an SOA-based platform supporting microservices.
# Designing and Implementing Weather Generators as Web Services
## 2 Addressing Bottlenecks of Legacy Weather Generators for Service
### Fundamental Principle of Stochastic Weather Generators  
Weather generators are models that use stochastic method for generating synthetic daily weather data (realizations). Long-term climate data are used to calibrate the stochastic model. Weather generators  
Figure 1: Overview procedures of performing weather generator in current situation.  
are often used to generate weather realization as input to crop models in order to study the impacts of climate variability to crop production [1]. Here, two stochastic weather generators were implemented as web services. The first model is used for stochastic disaggregation (temporal downscaling), called, DisAg [1]. The second model is a parametric downscaling method for probabilistic seasonal climate forecasts (SCF), called, predictWTD [8]. The predictWTD embeds the DisAg program inside of its algorithm which expands the capability of DisAg to downscale the full distribution of SCF. The details of the stochastic weather generators included in this paper are described in [1]. Some basic concepts are described here, in order to understand better the data requirement and data flow, which are important for designing and implementing them as web services. Figure 2 presents the schematic for DisAg. Long-term climate data that consist of solar exposure (SRAD), maximum temperature (TMAX), minimum temperature (TMIN), and rainfall (RAIN) are required for calibrating the stochastic models.  
The rainfall model in DisAg is a stochastic model with discrete (rainfall occurrence) and continuous (rainfall intensity) processes, as shown in Equation (1).  
\[R=f(\alpha_{c},\varnothing_{c}) \tag{1}\]  
where  
\(R\) _is rainfall function_  
\(f\) _is an operator that maps discrete and continuous model parameters with rainfall;_  
\(\alpha_{c}\) _is set of discrete model parameters based on climatology;_ and  
\(\varnothing_{c}\) _is set of continuous model parameters based on climatology._  
The discrete model is a rainfall occurrence model that decides the state of wetness or dryness on a day, based on a two-state Markov chain model. DisAg uses a hybrid two-state Markov model by which, if yesterday was wet, the probability that today is wet is based on 1 st order Markov model, while if yesterday was dry, it uses a 2 nd order Markov model. The continuous model in Equation (1) is a function for rainfall intensity, a hyper-exponential distribution, as used by DisAg.  
SRAD, TMAX and TMIN are generated based on a tri-variate normal model conditioned on rainfall [9].  
The predictWTD enhances DisAg's capability by downscaling the full distribution of seasonal climate forecast. Seasonal climate forecasts are usually issued in tercile probabilities, which are probability of below-normal (BN), near-normal (NN), and above-normal (AN) [10]. Climate forecasting can facilitate risk assessment, preparation and adaptation for global decision-making and planning in various sectors such as energy, water and food security [11].  
Figure 2: Overview functions of DisAg weather generator.
## 2 Data and Methods  
### Rain Gauge Data  
India has a network of about 7000 rain gauges installed and maintained by the India Meteorological Department (IMD). A daily gridded rainfall dataset using these rain gauge observations has been prepared by the IMD [40]. This gridded rain-gauge-based rainfall dataset is developed using inverse distance weighted interpolation method after rigorous quality check and also takes care of barriers and directional effects. This precipitation dataset is available at 0.25\({}^{\circ}\) spatial resolution since 1901 and reproduces the mean southwest monsoon rainfall features and spatial gradient of orographic rainfall more realistically and comparable with other existing daily gauge-based rainfall data sets such as earlier versions of IMD gridded products with coarser spatial resolutions and the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE) product [40]. Earlier versions of IMD gridded products and APHRODITE use a fewer number of rain gauges as compared to this gridded precipitation product. This daily gridded dataset showed a very high correlation coefficient of 0.99 and root mean square error (RMSE) less than 1 mm day\({}^{-1}\) in the southwest monsoon precipitation estimation as compared to APHRODITE and coarser spatial resolution IMD datasets. More detail about the procedures of development of this gridded rainfall dataset, such as quality control, spatial interpolation, rain gauge distribution, and its variability, can be found in [PERSON] et al. [40]. This rain-gauge-based daily gridded rainfall dataset for the monsoon period of 2010-2017 is considered as a reference in this study. The spatial distribution of rain gauges for a typical day is shown in Figure 1b. The rain gauge density is highest over the southern parts of the country, while northern and northeastern parts of India have rather poor rain gauge density. It is also to be noted that the number of daily reporting rain gauges varied with time, and it is between 2500 and 3000 during the study period. Although this gridded rain-gauge-based rainfall dataset might have some uncertainties due to spatial interpolation over rain gauge sparse area, it would mitigate the spatial representativeness error due to comparison of point observations from rain gauges and larger footprints of satellites. This gridded daily rain-gauge-based rainfall data has been widely used for the evaluation of multi-satellite precipitation products over India and for hydrometeorological and climatological applications [13; 23; 41; 42; 43; 44; 45; 46].  
Figure 1: (**a**) Topography (m) distributions across India, and (**b**) locations of 2725 reporting IMD rain gauges for a typical day in July 2014. Two rectangular boxes shown in Figure 1a indicate central India (CI) and west coast (WC) of India considered for detailed analysis.
# Design and Test of Real-Time Detection System for Cleaning Loss of Rapeseed Harvester
## 2 Materials and Methods
### Composition of Cleaning Material and Design of Signal Acquisition Device  
During the process of harvesting, the material enters the cleaning system after threshing and separating it using the drum, and then the cleaning material is thrown through the cleaning outlet at the rear of the fuselage after the cleaning screen [23]. The main components of the cleaning materials are rapeseed kernels, carob shells and short stems, as shown in Figure 2.  
Figure 1: Block diagram of loss detection system.  
Figure 2: Composition of cleaning materials: (**a**) kernels; (**b**) carob shells; (**c**) short stems.  
The signal acquisition device, as shown in Figure 3, mainly consists of a bracket, vibration isolation rubber pads, an inductive plate and sensitive components. Figure 3a shows a design drawing of the device that was created using the software Solidworks 2019. The picture in Figure 3b is an actual overhead view of the device. The vibration isolation rubber pad was mainly used to isolate the influence of environmental noise such as machine vibration. Epoxy resin adhesive was used to fix the sensitive components on the back of the center point of the inductive plate to increase the probability of collecting lost kernels, in which case the vibration signal detected by the inductive plate is most obvious [24]. The induction plate needs a certain strength and avoids resonance with the machine power unit. The size of the designed inductive plate is 150 mm long, 150 mm wide and 0.5 mm thick, and the material is a 304 stainless steel plate [25; 26].  
The sensitive element adopts YT-5L piezoelectric ceramics produced in Hebei, China. The specific parameters of the piezoelectric ceramics are shown in Table 1.  
The acquisition device for the impact signal is installed in the lower part of the end of the cleaning screen of the rapeseed combine harvester, and the lost kernels and impurities are discharged from the tail of the cleaning screen and impact on the inductive plate. In order to avoid the disturbance caused by the repeated bouncing of kernels and impurities on the inductive plate, the inductive plate plane forms an angle of 45\({}^{\circ}\) with the ground [27]. The test showed that the grain and residue bounced away from the sensor plate after impacting on the sensor plate, and there would be no secondary impact interference and other problems.
## 2 Results and Discussion
### Statistical Analysis  
Normality was assessed within the dataset for results determined by desorption with the same type of desorption solvent using the Shapiro-Wilk test. The initial results conformed to a normal distribution for every desorption solvent composition except for one, DS3n comprised methanol/water (80/20, \(v/v\)). Following log-transformation, a normal distribution was found in all groups; the testing null hypothesis confirmed the normal distribution of the results. Each group contained an equal number of results, and analysis of variance (ANOVA) performed with the Levene test confirmed the null hypothesis and homoscedasticity of the data. Fulfilment of these conditions allowed us to apply parametrical tests, such as the Pearson correlation coefficient or one-way ANOVA, which are vulnerable to deviations from normality. A one-way ANOVA showed no significant differences in the average values of the dependent variable within the analysed groups.
## 4 Method Validation
### Experiments
The standard explicitly states the following: \"The residual deformation must always be negligible\", i.e., at most, 5% of the maximum deformation in the presence of stress reached at the moment of the application of the forces. Figure 12: Positioning of the (**a**) first and second, and (**b**) third dial comparators. (**c**) Measurement points. Table 8 outlines the threshold values specified by the AFNOR regulation, demonstrating that the maximum residual deformations observed during testing remain well below the allowable limits. This confirms the validity of the measurements for the body test. The second and third tests follow a similar procedure, differing in the direction of the applied force and the corresponding positioning of the comparators. These tests involve loading the armrest in both positive and negative directions along the \(Y\)-axis, while the reference system remains consistent with the automotive coordinate system. Due to the change in test configuration, the sampling points differ from those specified in previous tests (as shown in Figure 12). Point A, located near the pneumatic piston's application area, remains conceptually unchanged. However, Points B and C are now positioned along the external edge of the body in decreasing order of height. Specifically, Point C is located at the center of the ribs in the lower part of the seat, where the seat cushion and backrest meet. Tables 9 and 10 show the measurements taken during the tests for Y-positive armrest push and Y-negative armrest push, respectively. Tables 8, 11 and 12 validate the accuracy and reliability of the measurements performed during the tests. However, from the other tables, it can be seen that the residual deformation measurements are not exactly increasing. This irregularity is attributed to settlement \"bumps\", which occurred due to the lifting of the probe shaft using the lifting knob. The data clearly indicates when these settlement bumps began, a phenomenon that was not anticipated in earlier tests. Despite this, the overall measures remain consistent and within acceptable thresholds, confirming the validity of the testing process. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**Applied Load**} & \multicolumn{2}{c}{**Point A**} & \multicolumn{2}{c}{**Point B**} & \multicolumn{2}{c}{**Point C**} \\ \cline{2-9}  & **[kg]** & **[N]** & **Displacement [mm]** & **Residual [-]** & **Displacement [mm]** & **Residual [-]** & **Displacement [mm]** & **Residual [-]** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 7: X-positive backrest push test results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Point A** & **Point B** & **Point C** \\ \hline Maximum displacement [mm] & 10.87 & 2.30 & 1.58 \\ Threshold 5\% [mm] & 0.54 & 0.12 & 0.078 \\ Maximum residual [mm] & 0.28 & 0.07 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Maximum displacement and threshold values in first and secondary tests.
## 4 Discussion  
This article investigated the applicability and effectiveness of real-time optimization error constraints in greenhouse model predictive controls. Although the constraint conditions in the traditional model predictive control have clear constraint ranges, this method did not effectively manage the constraints defined at each moment, and the constraints were not updated in real-time. From the simulation results and analysis, it can be seen that the appropriate constraint range had a significant impact on the optimal value and convergence speed of each iteration of the model predictive control. The method proposed in this article was based on the determination of the current tracking error, current environmental parameter settings, and current greenhouse temperature and humidity changes. Based on the prediction model, the greenhouse was predicted and tracked, and the simulation effect was relatively ideal.  
Compared with the traditional control algorithm, the advantage of this method was its predictive ability in general. The local optimal solution was obtained by establishing the objective optimization function and the corresponding constraints. From Figures 2-4, it can be seen that the model predictive control had advantages. However, adding some reasonable optimization constraints can make the model predictive control effect more ideal. From Figure 5, it can be seen that adding disturbances to the model is also applicable. From Figure 6, it can be seen that for the target loss, the convergence speed of this method is superior to other situations. The results showed that the proposed method can predict and track the environmental parameters of a greenhouse, improve the state increment, tracking error, control increment, and the target loss convergence speed of the model predictive control. There are still some shortcomings in this paper: 1. this article only verified the effectiveness of the proposed gradient descent combined with the two-norm method for optimizing constraint conditions. There was no discussion or comparison of optimizing results using different methods. 2. Although the simulation used a prediction  
Figure 7: The comparison of the output results between the traditional model predictive control and the model predictive control with optimization constraints.  
model established based on actual data and had good predictive performance in practice, the simulation did not use experimental data.