esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 5 Discussion
In addition, the approach that was developed in this study can be applied to any inaccessible regions where in situ data are not available, such as North Korea. For example, previous studies conducted classification over inaccessible regions applying training samples from different areas that have similar climate and environmental characteristics ([PERSON] et al., 2016) or unsupervised classification ([PERSON] et al., 2016). While machine learning-based classification requires training data, which is often challenging for certain areas, PMI-based paddy rice mapping does not require any in situ data for training. Although the validation accuracy of PMI based results was relatively lower than those of the machine learning approaches, the spatial distribution of paddy rice well matched that of the reference paddy rice data. PMI can be applied to different regions with the same threshold (greater than 0.2) or an adjusted threshold considering the dates of data acquisition. Consequently, PMI is considered to be useful to map paddy rice because PMI is calculated using a very simple equation and needs only two Landsat images or similar satellite data with NIR bands during the planting and harvest seasons. An annual update of paddy rice maps is possible using PMI. However, the availability of optical sensor data due to cloud contamination could be a limiting factor, especially for East Asia. This limitation can be overcome using geostationary satellite data, such as planned Geostationary Ocean Color Imager (GOCI)-2, which have high temporal resolution (-hourly) with 250 m spatial resolution. It is expected that fully clear images during two seasons can be successfully obtained every year using geostationary satellite data. Paddy rice mapping at a regional or continental scale can be conducted using PMI through Google Earth Engine with an aid of the continued improvement of computing capacity. However, in this study, paddy rice detected using PMI was somewhat confused with other crops where paddy rice is planted in dry fields. It is one of the limitations of using PMI, in that PMI can be successfully applied only where paddy rice is planted in flooded fields.
## 4 Overview of Recent Applications
The detected and classified paint defects/damages were subsequently digitalized and mapped to the three-dimensional geometric reconstruction of the structure. These developed functionalities allowed the digital twin of the structure to be updated with manufacturing-induced geometric deviations and paint defects/damages using inspection images at regular time intervals. [PERSON] et al. [21] presented the development of seismic fragility curves for a precast reinforced concrete bridge in the Dominican Republic, instrumented with a structural health monitoring (SHM) system. In this research, the digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that were consistent with the seismic fault and site characteristics. [PERSON] et al. [6] presented and examined the efficacy of a framework for automated model calibration using a measured operational and ambient structural response for the development of a precise digital twin of a physical structure. The guidelines provided in this research aim to assist in choosing the right model class for accurate response prediction. The study used an in-service, double-track, riveted steel plate girder railway bridge as a testbed for the proposed framework. In the research of [PERSON] et al. [27], a 5G Next Generation Radio Access Network (5G-NG-RAN) assisted the cloud-based digital twins' framework to virtually monitor wind turbines and form a predictive model for forecasting wind speed and predicting the generated power of wind turbines. Further recent applications on infrastructures, mainly bridges, can be found in [22; 23; 24; 25; 37; 38]. A schematic representation of a DT that can help predict fatigue in a hydropower plant is shown in Figure 6. The whole hydraulic network that connects two reservoirs at different levels and is used for storage of energy can be modelled by using classical one-dimensional reduced models. If pressure deviations at a given point are considered to be high, a detailed finite element model can be used to assist damage and fatigue prediction.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 4 Algorithm Verification  
### Simulation Verification  
#### 4.1.1 Mass Estimator Simulation Analysis  
To verify the accuracy of the quality estimation algorithm, a co-simulation was executed utilizing Simulink and TruckSim. A forgetting factor \(\lambda=0.98\) was incorporated to mitigate the impact of historical data, with an adjustable parameter \(h=150\). The vehicle employed a constant acceleration of 3 m/s\({}^{2}\) and functioned on a level road. The vehicle used for testing was a light commercial model with a full load mass of 1000 kg. Tests were performed under three distinct conditions: unloaded (0 kg), half loaded (500 kg), and fully loaded (1000 kg). The specifications of the vehicle are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Vehicle mass (unladen) m & 3030 & kg \\ Vehicle load & 0/500/1000 & kg \\ Gravitational acceleration \(g\) & 9.8 & m/s\({}^{2}\) \\ Air density \(\rho\) & 1.206 & Kg/m\({}^{3}\) \\ Windward area \(A\) & 6 & m\({}^{2}\) \\ Air resistance coefficient \(C_{d}\) & 7 & \\ Transmission efficiency \(\eta\) & 0.9 & \\ Transmission gear ratio \(i_{g}\) & 9.5 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle parameters.  
Figure 5: Vehicle roll dynamics model.  
It can be observed from Figure 6 that at the beginning of the experiment, due to the large input torque and given that the acceleration has not yet reached the expected level, the experimental value deviates significantly from the real value. This phenomenon reflects the lag of the system response and the instability of initial conditions. However, as the input parameters gradually stabilize, combined with the iterative optimization of the algorithm and the introduction of the forgetting factor, the estimated value begins to slowly and gradually approach the real value of the mass at about 6 s. At 15 s, the estimation process is completed with convergence. Specifically, the vehicle mass, when empty, ultimately converges to 3042 kg, possessing an error margin of 0.39%. In the half-load scenario, the mass converges to 3554 kg, with an error of 0.67%. Under full-load conditions, the mass converges to 4067 kg, exhibiting an error of 0.91%. These results indicate that the designed mass estimator can achieve good estimation accuracy under different conditions. According to the general evaluation criteria, when the estimation error is within 5%, the estimation result can be considered accurate. Therefore, drawing from the preceding analysis, we determine that this mass estimator is capable of efficiently conducting estimations in practical applications and fulfills the genuine requirements of engineering.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 3 Health Optimization Management
### Problem Description
Based on the traditional CVT construction algorithm, the insertion construction method was introduced, so that in the original CVT configuration, when the robot number is reduced, all the triangles whose circumscribed circle contains new points are recorded, the common edges that affect the construction of the new triangle are deleted, and then new edges are re-planned, until all points are inserted. The specific process is shown in Figure 3a-d. The specific control Algorithm 2 steps in this study were as Algorithm 2. ```
0: The initial position of a group of robots \(P:\{p_{i}\}_{i=1}^{n}\), \(h_{i}(t)\), \(h_{i}(0)\); Boundary information \(\partial Q\) of task area \(Q\), area density function \(\rho(q)\); Procedure:
1: Calculate the centroid of the Voronoi cell \(\{p_{i}^{*}\}_{i=1}^{n}\) and \(\{V_{i}\}_{i=1}^{n}\) of the robot position \(P\);
2: While\(((p_{i}-p_{i}{}^{*})\geq\epsilon)\&\epsilon\&\partial(E_{V}(p))\
eq 0\)
3: Calculate \(d_{i}(t)\) and \(h_{i}(t)\) for each robot
4:if\(h_{i}(t)\leq 0\)then
5: Use the insert construction method to re-form and divide the area
6:endif
7: Calculate \(h^{*}(t)\) and \(u_{i}(t)\) for the entire formation
8: Drive the robot to the center-of-mass position \(p_{i}=p_{i}{}^{*}\), \((i=1,\ldots,n)\);
9: Generate a new set of robot positions \(P:\{p_{i}=p_{i}^{*}\}_{i=1}^{n}\);
10: Calculate the new centroid \(\{p_{i}^{*}\}_{i=1}^{n}\) and \(\{V_{i}\}_{i=1}^{n}\) of the robot position set \(P\);
11:End While
```  
**Algorithm 2**CVT-based multi-robot formation control and health optimization management  
Figure 3: Insert construction method: (**a**) robot P has a health value of zero; (**b**) delete the public edge related to P; (**c**) re-plan neighboring robots; (**d**) form a new distribution. When the formation is moving, the entire formation is recalculated to obtain the distribution in the environment. While controlling the robot to have the smallest possible impact on other robots, making the robots in the formation respond better to changes in the environment and improving the robustness and task execution ability of the formation in various environments.
## 4 Results and Discussions
### Evaluation Metrics
#### 4.1.4 Mean Average Precision  
Mean Average Precision (MAP) is mathematically defined as:  
\[\text{Mean\ Average\ Precision}=(1/n)\sum_{i=0}^{n}p(C_{i}) \tag{9}\]  
\(p(C_{i})\) represent precision for \(i^{\text{th}}\) customer where '\(n\)' is the total number of customers concerned in the experiment.
# Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification
## 3 Results and Discussion  
### Performance Evaluation Parameters  
To evaluate the performance of our ESN model, the study used three performance parameters which are recommended by AAMI standards for evaluating the performance of learning algorithm, which includes sensitivity (Se), classification accuracy (Acc), and error rate (Er)  
\[\mathbf{S_{e}}=\frac{\mathbf{TP}}{\mathbf{TP}+\mathbf{FN}}*\mathbf{100} \tag{12}\]  
\[\mathbf{Acc}=\frac{\mathbf{TP}+\mathbf{TN}}{\mathbf{TP}+\mathbf{TN}+\mathbf{FP}+\mathbf{FN}}*\mathbf{100} \tag{13}\]  
\[\mathbf{E_{r}}=\frac{\mathbf{FN}+\mathbf{FP}}{\mathbf{N}} \tag{14}\]  
where **True negative (TN)** is defined as the number of normal records, which is correctly classified as normal, **True positive (TP)** is defined as the number of abnormal records, which is correctly classified as abnormal, **False positive (FP)** is defined as the number of normal records that are classified as an abnormal record of the dataset, and **False Negative (FN)** is defined as the number of abnormal records that are classified as normal.
## 3 Results and Discussion
### Influence of Ambient Conditions--Surrounding Temperature and Ventilation (Parameter Sweep Study)
#### 3.3.2 Influence of Air Movement Around the Pack  
The convection coefficient (Figure 5b) was varied in the typical range of free, non-forced convection between almost no air movement (1 W/m\({}^{2}\) K) and a strong wind (20 W/m\({}^{2}\) K). For a high level of air movement (10 and 20 W/m\({}^{2}\) K), the heat is very quickly dissipated by the surface, and the pack temperatures are low during use (70.5 and 68.1 \({}^{\circ}\)C) and even decrease during charging for 20 W/m\({}^{2}\) K. This demonstrates that the heat transfer ability of the cooling system is able to transport the created thermal energy during charging if the pack surface is sufficiently cooled. The fully charged state is reached without interruption after 106.5 and 84.5 min. A low level of air movement leads to higher temperatures and a significant prolongation of cool down and charging. At 5 W/m\({}^{2}\) K, a maximum temperature of 72.3 is reached, and the charging is interrupted once. The pack is fully charged after 169.3 min. Regarding 1 W/m\({}^{2}\) K, the heat dissipation by the surface is very limited, and the pack heats up quickly during charging (73.6 \({}^{\circ}\)C), mainly governed by the heat capacity of the material. The cool-down phase is very slow. The charging start temperature is reached after 437 min, and the charging process is interrupted several times because the stop temperature is reached. The pausing interval is also prolonged by the slow cool-down speed. The fully charged state was not reached within the simulated maximum time of 500 min.
## 2 Literature Review
### PDCA Integration with Quality Management Systems  
The popularity of the PDCA cycle extends beyond other improvement methodologies; it is deeply ingrained in most major quality management systems. Standards such as ISO (International Organization for Standardization) 9001, ISO 14001, and ISO 45001 have readily embraced and integrated it. This section explores the integration of PDCA within various quality management systems, as follows [29; 30; 31]:  
* ISO 9001: The ISO 9001:2015 standard [32] leverages the PDCA cycle to illustrate how its clauses 4 to 10 can be systematically grouped and implemented (Figure 5). This serves as a powerful testament to the PDCA cycle's effectiveness and versatility in driving continuous improvement within quality management systems [33; 34; 35].
* Adaptability: Research by [PERSON] and [PERSON] [36] demonstrated this adaptability by developing an implementation guideline for manufacturing companies. This guideline, rooted in the PDCA model, effectively integrates ISO 9001 requirements with Industry 4.0 technologies. It showcases how PDCA can bridge the gap between established quality management systems and the latest technological advancements.
* Service industry: [PERSON] and [PERSON] [28] meticulously analyzed the ISO/IEC (International Electrotechnical Commission) 17025:2017 standard clause by clause, exploring its connection to the PDCA concept within the process approach framework. Their findings suggest that ISO/IEC 17025:2017 incorporates the principles of the PDCA model. The standard's specific clauses address each stage within the overall framework of a laboratory's quality management system (Figure 6).
* IATF (International Automotive Task Force) 16949: This standard requires organizations to have a documented problem-solving process to prevent recurrence in clause 10.2.3. Subclause c) of this clause specifies that this includes root cause analysis, the methodology used, analysis, and results [37]. The 8 Ds technique is documented in the literature as a widely accepted technique for meeting this requirement [38; 39]. The PDCA cycle has served as a reference due to its strong relationship with the 8 Ds, as it has functioned as an advanced approach to decision-making and problem-solving [40; 41].
## 5 Evaluation
### Cold Starts
#### 5.3.1 Measurement Methodology  
The established measurement methodology consists in generating and collecting 300 cold start execution records of both the cloud-agnostic and the cloud-non-agnostic functions, in each cloud provider. This methodology requires a waiting time of at least 15 to 20 min after the latest execution to obtain a single data record. This interval allows the container to have enough time to transition from an idle to a stopped (cold) state. This means that if we were to trigger a FaaS resource to obtain a single cold start execution record at a time, each function test would take around 100 h to retrieve the 300 data records (_300 x 20 min_). To overcome this issue, we first deployed 100 FaaS resources in each cloud provider, that is, 100 cloud functions in GCP and 100 _function apps_ in MsAzure. We then used three batches of 100 invocations to trigger the 100 deployed functions one at a time in each cloud provider. The invocation batches were separated by an interval of about an hour to ensure that cold starts would occur.  
In MsAzure, cold starts happen per _function app_, meaning that once an azure function is cold started, any other function within the same app can be warm started if executed shortly after the cold start. Therefore, the fastest way of collecting 100 cold start execution records is by cold starting 100 different _function app_ instances, each one containing a single azure function. Due to some unanticipated problems in cold starting each _function app_, to be discussed in Section 5.6, we ended up only doing 50 executions per batch in MsAzure for 50 _function apps_. This issue caused the process of data collection in MsAzure to take the double the amount of time of GCP. Nevertheless, we were still able to retrieve the 300 records for each function.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Google Cloud Platform** & **Microsoft Azure** \\ \hline Location & europe-west1 (Belgium) & west europe (Netherlands) \\ Runtime & Java \(11\) & Java \(11\) \\ Memory allocated & 256 MB & \(1.5\) GB \\ Min/Max instance count & 0–3000 & 0–200 \\ Operating system & Ubuntu 18.04 & Windows \\ \hline \hline \end{tabular}
\end{table}
Table 1: Function execution environment specifications.
## 4 Calculation Method of Shear Carrying Capacity of UHPFRC Beam-Column Joint
### Components of Shear Carrying Capacity
#### 4.2.3 Shear Force Carried by Stirrup in Joint Core  
In general, the effects of stirrups are asynchronous owing to the different crack positions and widths in the joint, and not all stirrups yield completely when the joint fails. Hence, the factor \(\psi\) is incorporated into the calculation for the contribution of stirrups in UHPFRC beam-column joints. Therefore, the shear carrying capacity \(V_{sv}\) of stirrups can be expressed as  
\[V_{sv}=\psi f_{sv}A_{sv}\frac{h_{0}-a_{s}^{\prime}}{s} \tag{12}\]  
Figure 9: \(\eta\) versus \(\lambda_{f}\) relation curve.  
In Equation (12), \(f_{yy}\) is the stirrup yield strength; \(A_{sv}\) is the sum of each section area of the stirrups in the same vertical joint section; \(s\) is the spacing of stirrups; \(h_{0}\) is the effective height of the beam section adjacent to the joint; \(a_{s}^{\prime}\) is the minimum distance from the compressive longitudinal reinforcement axis to the extreme section edge in beam; and \(\psi\) is the non-uniformity coefficient of the stirrups, which varies between 0.85 and 1. When the stirrup characteristic value (\(\rho_{sv}f_{yy}/f_{c}\rho_{sv}\) is volume stirrup rate of joint, \(f_{yy}\) is the strength of stirrup, and \(f_{c}\) is the strength of RPC) is greater than or equal to 0.25, \(\psi=0.85\) should be used; when the stirrup characteristic value less than or equal to 0.1, \(\psi=1.0\) should be used; for all other cases, \(\psi\) should be computed with linear interpolation method according to the stirrup characteristic value of joint.  
Combining Equations (3), (7), (11) and (12), the shear carrying capacity \(V_{j}\) of the UHPFRC beam-column joints can be expressed as  
\[V_{j}=3.35\sqrt{f_{c}^{\prime}}A_{str}\cos\left[\arctan(\frac{h_{0}^{\prime \prime}}{h_{c}^{\prime\prime}})\right]+(16.447-14.30\lambda_{f}^{0.061}) \lambda_{f}h_{b}b_{c}+\psi f_{yy}A_{sv}\frac{h_{0}-a_{s}^{\prime}}{s} \tag{13}\]  
In order to account for the influence of joint type, factor \(\gamma\) is introduced into Equation (13), which can be expressed as  
\[V_{j}=\gamma\bigg{\{}3.35\sqrt{f_{c}^{\prime}}A_{str}\cos\left[\arctan(\frac{ h_{0}^{\prime\prime}}{h_{c}^{\prime\prime}})\right]+(16.447-14.30\lambda_{f}^{0.061})\lambda_{f}h_{b}b_{c}+\psi f_{yy}A_{sv}\frac{h_{0}-a_{s}^{\prime}}{s} \bigg{\}} \tag{14}\]  
in which \(\gamma\) is 0.8 and 1 for exterior and interior joints, respectively.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Approximate Entropy  
Approximate entropy (ApEn) quantifies the regularity of a time-series signal by determining the likelihood that a pattern of similar data points within a tolerable range of one another, r, will continue to be similar for a chosen number of observations, m [33; 35]. It is well suited for small datasets, which nicely frames the measure to be used on small windows of a larger dataset to obtain a moving representation of the regularity of a signal. \(ApEn\) is defined as follows [33]:  
\[-ApEn=\Phi^{m+1}(r)-\Phi^{m}(r) \tag{2}\]  
where \(m\) and \(r\) are fixed values, \(m\) being the length of compared runs, \(r\) serving as a filter value, and \(\Phi^{m}(r)\) is defined as:  
\[\Phi^{m}(r)=\left(N-m+1\right)^{-1}\sum_{i=1}^{N-m+1}\ln[C_{i}^{m}(r  
The value \(m\) is a positive integer that represents a window of data to be compared. A value of two is very commonly used in various applications of ApEn and is what was used in this study [33]. The value to use for \(r\), however, is much less agreed upon. It is a filter value that must be a real positive number. A general rule that is loosely advised is to use 0.2 times the standard deviation of the time-series data, with the understanding that different applications of ApEn will require different filter levels. In this research, a large variety of \(r\)-values were tested, including various proportions of the standard deviation as well as constant values. The \(r\)-value that was chosen was the one that highlighted the largest difference in COP ApEn between the most stable and the least stable quiet standing positions, which was a value of 10. This value effectively filtered the redundant data associated with the collection frequency of 1200 Hz by allowing for successive data points to be further from one another and in a larger window of possible values while still being considered a part of a pattern. Since COP oscillates regularly in highly stable conditions, ApEn calculations need to account for the sway with a larger \(r\)-value than is typically suggested.  
Moving approximate entropy was calculated for all COP trials, in both ML and AP directions, using windows of size 1800, or 1.5 s intervals, with a 50 percent overlap to create 39 segments. This allowed for a visual understanding of how the approximate entropy changed over time; however, these 39 values were averaged to obtain a single approximate entropy value for further calculations. This was conducted as numerous studies have shown postural sway to be a stationary signal in durations greater than 15 s [19; 44; 45].
## 1 Introduction
### Literature Review
[32] to overcome the drawbacks of FPP methods and involves the extension of robust optimization from stochastic algorithms to fuzzy algorithms. RFPP considers three sections in objective function: (i) the minimization of the weight sum of the expected objective values; (ii) the difference between two possible extreme objective values; and (iii) the penalty for constraint violation as the objective function [27; 33]. It has, however, been limitedly applied to EES planning. Generally, although previous research works can effectively deal with EES issues under multiple uncertainties, several gaps still need to be remedied. Firstly, few of the previous studies considered the traffic sector, which has been identified as a growing contributor to air pollution. Currently, a series of traffic policies have been adopted to alleviate the air pollution caused by motor vehicles. However, it remains unclear how much pollution reduction can be achieved by these control measures and policies. Secondly, most of these studies are incapable of considering the system risks from the stochastic and fuzzy uncertainties during the optimization process, which may lead to significant deviations in the optimized decision schemes, even in the event of system optimization failure. Thirdly, the RFPP method is commonly used to plan water resource allocation, and solid management, and is scarcely applied to plan EES systems.