esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussion
### Volumetric Strain Characteristics  
To investigate the influence of the end restraint on the volumetric strain behavior of the specimens during the drained shear tests, the volumetric strain (\(\epsilon_{v}\)) versus axial strain (\(\epsilon_{a}\)  
Figure 6: Mohr stress circle and linear strength envelope diagrams of coarse-grained soil: (**a**) End restraint; (**b**) End free (microfriction).  
Figure 7: Mohr stress circle and nonlinear strength envelope diagrams of the coarse-grained soil: (**a**) End restraint; (**b**) End free (microfriction).  
curves obtained from the specimens under the end-restraint and end-free (microfriction) conditions were compared, as shown in Figure 8. Under different end-restraint conditions, the coarse-grained soil exhibited volumetric dilation at low confining pressures and volumetric contraction at high confining pressures. The end restraint had a significant influence on the volumetric strain characteristics of the coarse-grained soil. At a low confining pressure (0.2 MPa), the end deformation of the end-restraint specimen was smaller, and the swelling in the middle of the end-restraint sample was not significant compared with that of the end-free (microfriction) specimen. The overall volume of the specimen was reduced, and the tendency for the volumetric contraction of the end-restraint sample was more evident in the initial stage of shearing. With shearing, the bulging in the middle of the specimen further intensified, and the specimen as a whole showed volumetric dilation. At this time, the dilatation tendency of the end-free specimen (microfriction) was more evident. At high confining pressures (0.4, 0.6, and 0.8 MPa), the volumetric strain of the specimen with the end restraint was always greater than that of the end-free specimen, which showed a strong volumetric contraction tendency during the whole shearing process.
# Energy Productivity Potential of Offshore Wind in Poland and Cooperation with Onshore Wind Farm
## 4 Conclusions  
The offshore wind sector is developing dynamically in Europe, while offshore wind investments in Poland are only just beginning. The first planned offshore project will be commissioned after 2025. Currently, support for offshore wind projects has been established in Poland and covers 6 GW of installed capacity in the first stage. Based on these expectations, this research was conducted based on the potential impact of such capacity on the power system.  
In Baltic Sea conditions, offshore wind is distinguished by its high-capacity factor compared to onshore, with a capacity factor of 30.1% for onshore wind and a potential of 55.6% for offshore wind. This shows a clear advantage for offshore over onshore.  
Due to the variability of wind speeds, the guaranteed capacity of wind power plants is considerably lower than that of other conventional electricity generation technologies. Nevertheless, a certain amount of installed wind power capacity contributes to the system's power demand.  
During the emergency period examined in the research, with a potential of 6 GW of offshore capacity planned for construction, these sources did not significantly improve the security of the energy supply. In the emergency period, these sources did not supply power to the system due to weather conditions such as low wind speeds that fell below the cut-in wind speed for the examined Baltic Sea location.  
However, in the period of the year considered in this study, these sources will generate electricity that will reduce emissions by approximately 22.4 million metric tons of CO\({}_{2}\) by eliminating the burning of 13.4 million metric tons of hard coal on an annual basis.  
In the wind speed model considered, based on ERA5, the potential electricity production from these sources will be 29.2 TWh/year (2022 data), representing a 17% contribution to energy demand. At this scale, the 6 GW of installed capacity of these sources will exceed the installed capacity of PV and onshore combined today by 1 TWh (about 12 GW installed in PV sources and about 8 GW in onshore wind). Both onshore and offshore wind are ways to reduce emissions, save fossil fuels, and reduce energy costs.  
The further direction of research will be  
* A detailed analysis of the influence of the wind turbine high for productivity factor;
* Economic analyses--comparison between onshore and offshore wind installation.  
Methodology, P.O. and T.S.; Validation, T.S.; Formal analysis, T.S.; Investigation, P.O.; Data curation, P.O.; Writing--original draft, P.O. and T.S.; Writing--review & editing, T.S.; Visualization, T.S.; Funding acquisition, P.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The author declares no conflict of interest.
## 2 Materials and Methods  
### Soil Sampling and Measurement of Water Content and Bulk Density  
Table 1 presents a summary of the properties of in situ soil samples used in this study, along with the measurement results of two target variables: water content and bulk density. This study selected three different locations, denoted as A, B, and C, each with soils of different textures: silt loam (SiL), sandy loam (SL), and loamy sand (LS), respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Sampling** & **Texture** & **Location** & **Number of** & & & **Water Content (\%)** & & & **Bulk Density (kN/m3)** & \\
**Point** & **Texture** & **Location** & **Images** & **Max** & **Mean** & **Min** & **Range** & **Max** & **Mean** & **Min** & **Range** \\ \hline A & SiL & \(37^{\circ}16^{\prime}09.7^{\prime\prime}\) N & & 128 & 17.8 & 9.2 & 1.2 & 16.6 & 12.7 & 10.9 & 7.8 & 4.9 \\  & & \(37^{\circ}16^{\circ}15.6^{\prime\prime}\) N & & 144 & 22.3 & 12.7 & 4.6 & 17.8 & 14.7 & 11.2 & 8.3 & 6.4 \\ C & LS & \(37^{\circ}27^{\circ}28.5^{\prime\prime}\) N & & 96 & 21.6 & 14.7 & 9.6 & 12.0 & 16.2 & 12.4 & 9.1 & 7.1 \\  & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil water content and bulk density of in situ soil samples.  
To obtain data on the soil surface, 128, 144, and 96 images were taken at points A, B, and C, respectively. Following the image capture, soil sampling was conducted using a metal cylinder with a diameter of 50 mm and a height of 51 mm. To ensure comprehensive coverage of the area of the soil surface image, four samples were collected at each location in a \(2\times 2\) arrangement using four metal cylinders. Therefore, 512, 576, and 384 soil samples were collected at points A, B, and C, respectively. The WC and BD for each location were computed by taking the average of four samples. Accordingly, there existed 128, 144, and 96 data for WC and BD at points A, B, and C, correspondingly, matching the number of digital images. WC and BD were determined by Equations (1) and (2), respectively.  
\[\text{WC}(\%)=\frac{W_{w}}{W_{s}}\times 100=\frac{W_{2}-W_{3}}{W_{3}-W_{1}} \times 100 \tag{1}\]  
\[\text{BD}\Big{(}\text{kN}/\text{m}^{3}\Big{)}=\frac{W_{s}}{V_{s}} \tag{2}\]  
where \(W_{w}\) is the weight of water, \(W_{s}\) is the weight of dry soil, \(W_{1}\) is the weight of the container (metal cylinder), \(W_{2}\) is the weight of the container with wet soil, \(W_{3}\) is the weight of the container with dry soil and \(V_{s}\) is the volume of soil (same as the volume of the container). To measure the weight of dry soil, the soil samples were completely dried at 110 \(\pm\) 5 \({}^{\circ}\)C for 24 h using a drying oven (Changshin Science C-DOD3, Seoul, Republic of Korea). WC ranged from a low of 1.2% to a high of 22.3%, ranging from very dry soil to moist soil. BD also includes loose to dense soils, from a minimum of 7.8 kN/m\({}^{3}\) to a maximum of 16.2 kN/m\({}^{3}\).
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 5 Experimental Results and Discussions
### Data Sources Description  
We connect our ECG patch to the TechPatient CARDIO V4 heart simulator to collect ECG data to generate ECG datasets. This way, we avoid connecting the prototyped hardware to an actual patient. The simulator can generate real-time ECG waveforms for different cardiac conditions and support two modes of operation: ECG mode and Rhythmic mode. The ECG mode provides realistic 12-lead ECG waveforms. The rhythmic mode simulates 45 predefined arrhythmias or heart diseases, such as ventricular tachycardia and ventricular fibrillation. The device can be configured in 1 beat per minute (BPM) increments from 20 to 240 BPM and 2 BPM increments from 240 to 300 BPM in the ECG mode. Therefore, we created two datasets using the ECG Simulator: normal and abnormal ECG signals to test the XBeats platform.  
On the other hand, we use a larger dataset (i.e., PTB-XL) for training and validating the developed classification algorithms and a dataset we created from the TechPatient CARDIO V4 heart simulator [41] as an external source of data for additional validation. The purpose of using the PTB-XL is to extend the developed classification model to cover a broader range of heart diseases and provide better accuracy. The PTB-XL dataset [38] provides a freely accessible ECG dataset of unprecedented size hosted by PhysioNet. The dataset comprises 21,837 clinical 12-lead ECG measured simultaneously, representing the conventional 12-lead ECG records. The length of each record is 10 s. The tests were performed on 18,885 subjects (52% were male, and 48% were female). The digitized signals are available at two sampling frequencies, 500 and 100 samples per second. Each record in the dataset has an attached header file describing each subject's demographic information, health conditions, doctor's comments, age, gender, diagnoses, number of records, number of samples, and the sampling rate. The records are categorized into five superclasses (NORM: normal ECG, CD: conduction disturbance, MI: myocardial infarction, HYP: hypertrophy, and STIC: ST/T changes) from which a 24-subclasses are derived, forming a multitude of diverse ECG data as a resource for ECG analysis algorithms. Accordingly, we aggregate all the abnormal signals (24 different abnormal heartbeat classes) into one category to make up a binary data set for normal and abnormal conditions. The size of the PTB-XL dataset makes it a valuable asset in machine learning and deep learning applications.
## 3 Multi-Objective Dynamic Mission Scheduling Algorithm (MODMSA)  
In this section, the multi-objective dynamic mission-scheduling algorithm (MODMSA) is introduced in detail. The general framework of the MODMSA is described first, and the improved evolutionary operators in the SPEA2 are then proposed. Afterwards, a profit-oriented local search algorithm is illustrated, and an adaptive perturbation control strategy is presented.
# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 5 Results
### Predicting Pass/Fail Students
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **February 2018** & **March 2018** & **April 2018** & **May 2018** & **June 2018** & **July 2018** \\ \hline Naïve Bayes & 53.33 & 61.67 & 61.67 & 63.33 & 67.78 * & 67.78 * \\ \hline Random Forest & 52.78 & 54.44 & 60.56 & 60.00 & 61.67 & 60.56 \\ \hline Bagging & 57.22 & 57.78 & 60.56 & 61.11 & 66.67 & 62.22 \\ \hline PART & 62.22 * & 63.33 * & 58.89 & 62.22 & 55.00 & 52.22 \\ \hline SMO & 56.11 & 61.67 & 64.44 * & 65.56 * & 64.44 & 63.89 \\ \hline IBk-5 NN & 56.67 & 58.89 & 60.56 & 60.56 & 60.00 \\ \hline Auto-WEKA & \begin{tabular}{c} 61.11 \\ PART \\ \end{tabular} & \begin{tabular}{c} 73.33 \\ J48 \\ \end{tabular} & \begin{tabular}{c} 66.67 \\ OneR \\ \end{tabular} & \begin{tabular}{c} 69.44 \\ LMT \\ \end{tabular} & \
\begin{tabular}{c} 71.11 \\ J48 \\ \end{tabular} &
\begin{tabular}{c} 70.56 \\ JRip \\ \end{tabular} \\ \hline \hline \end{tabular} \(t\)-test: \(p\)-value = 0.0317, a = 0.05.  
\end{table} 
Table 9: Overall accuracy results regarding the “Physics III” course and Pass/Fail classification task. a few. Overall, tree-based classifiers were suggested the most. More specifically, the LMT method was the outperformer 8 out of 17 times. Finally, by applying the \(t\)-test on the results as shown in Tables 8-10, we obtained the following \(p\)-values: \"Physical Chemistry I\" \(p\)-value = 0.0365; \"Physics III\" \(p\)-value = 0.0317; Analytical Chemistry Laboratory \(p\)-value = 0.0223. Thus, we can conclude that the autoML presents a statistically significant increase when applied to specific educational datasets.
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 6 Conclusions  
This paper employs obstacle avoidance steering control theory in the context of tracked robots, mitigating the impact of skidding and slipping on their driving control and enhancing the effectiveness of obstacle avoidance steering.  
Thia paper analyzed the correlation between the theoretical and practical steering radii and the speed of the two tracks while accounting for the impact of skid and slip. Relationship models were developed linking track speed, driving force, and track turning radius. The research delved into the spatial requirements for robot steering, identifying the maximum and minimum steering radii essential for the robot's steering maneuvers. A corresponding relationship was established between the robot's steering angle and the presence of side obstacles. A correction model of the tracked robot was established. Additionally, this paper delved into LiDAR's detection principle and constructed a robot measurement and control system comprising sensing and signal processing layers, decision control layers, and motion execution control layers. Virtual prototype simulation models for both the tracked robot and obstacles were developed using ADAMS. The simulation experiments encompassed center steering and differential steering, with the finding that employing the differential steering method, based on surrounding obstacle and road surface information, enhances the steering efficiency of tracked robots. This paper concludes by highlighting the use of theoretical guidance to conduct obstacle avoidance steering experiments with the robot, effectively validating the accuracy of the theoretical analysis.  
In this paper, an obstacle avoidance steering control strategy based on road parameters and the position of obstacles is proposed, but some errors are admitted. Future research should focus on the online estimation of road parameters and the further development of obstacle avoidance steering control algorithms.  
Conceptualization, C.W. and H.M.; methodology, C.W.; formal analysis, X.X., P.L. and H.T.; software, H.Z. and S.W.; writing--review and editing, C.W.; project administration, C.W. and H.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China, grant numbers 51975468, 52174149, and 52374161; the Natural Science Basic Research Program of Shaanxi, China, grant number 2023-JC-YB-331; and the Shaanxi Provincial Department of Education to Serve Local Special Program Projects, grant number 22 JC051.  
Not applicable.  
The data presented in this study are available on request from the first author.  
The paper acknowledges the support from the \"Youth Innovation Team of Shaanxi Universities\" and the \"Innovation Team for Intelligent Measurement and Control of Coal Mine Electromechanical Systems\".  
The authors declare no conflict of interest.
## 2 Dispersive Fourier Transformation Technique
### Theory of DFT
#### 2.1.2 Impact of Higher-Order Dispersion  
Equation (3) provides the theoretical basis for the standard DFT, where the second-order dispersion approximation is applied. Therefore a unique linear one-to-one mapping between the optical frequency and time is created thanks to the linear group delay response with respect to the frequency. If higher-order dispersion is taken into account [54; 55; 56], the frequency-to-time mapping process is still valid so as long as the temporal Fraunhofer approximation is satisfied. In this scenario, however, the mapping is no longer linear due to the higher-order frequency dependency of the group delay. A modified nonlinear mapping relation incorporating higher-order dispersion is given [49]  
\[\omega=\frac{t}{\dot{\Phi}_{0}}-\sum\limits_{k=3}^{\infty}\left[\frac{\dot{ \Phi}_{0}}{\left(k-1\right)!\left(\dot{\Phi}_{0}\right)^{k}}\,t^{k-1}\right] \tag{4}\]  
where \(\dot{\Phi}_{0}^{k}\) is the \(k\)-th order dispersion coefficient.
## 2 Background
### Edge Computing Overview
#### 2.3.2 Standardization Overview  
Edge computing is a quite fragmented and evolving ecosystem, and additionally, the standards and business models are in the phase of maturing. This section aims to give a quick overview of the status of the main standardization directions.  
The global standardization body for mobile communication technology is 3 GPP. Several working groups within 3 GPP are focusing on edge computing. In the 3 GPP SA2 working group, the technical specification on 5G system enhancements for edge computing [8] and 5G system architecture [9] specify the details on how user traffic is routed to the appropriate edge application server within 5 GS, covering edge application server discovery, UPF selection and connectivity models that enable edge computing for edge-unaware devices. In the 3 GPP SA6 working group, TS 23.558 [10] defines an architecture for edge computing support of the devices with edge-aware capabilities. The proposed architecture includes an edge enabling layer, which facilitates the communication between the application clients and the servers (e.g., optimized edge application server discovery, network exposure capabilities towards the edge application server). In the 3 GPP SA5 working group, a study is running on the life-cycle management aspect of the application servers in the edge cloud [11].  
The European Telecommunications Standards Institute (ETSI) multi-access edge computing (MEC) initiative has created an open and standardized information technology (IT) service environment, which allows third-party applications to be hosted at the edge of the mobile network and which is capable of exposing network and context information. It specifies a framework for service delivery, Application Programming Interfaces (API) for exposure and programmability, as well as covering the management and orchestration operations. The ETSI MEC studies federated architecture to support multi-operator, multi-vendor scenarios. The ETSI MEC also makes efforts to propose a synergized architecture leveraging the ETSI MEC and 3 GPP specifications [12].  
The Global System for Mobile Communications Association (GSMA) specifies an end-to-end high-level architecture and provides an edge cloud service description mainly from the telco operator perspective. GSMA also describes stakeholder roles and different business models for telco operators in the edge ecosystem.  
The 5G Alliance for Connected Industries and Automation (5G-ACIA) also investigates the role and capabilities of edge computing; its enablers and architecture are analyzed, together with how edge computing can be applied to industrial use cases.
## 3 Results  
The stationary dose distribution (Figure 5a) was the most uniform of all the distributions presented, resulting in an \(H_{RMS}\) of at least 96% (Table 1). When the moving film was irradiated during FB without motion mitigation (Figure 5b-e), the dose distributions showed acute distortions that were manifested as hot and cold spots (over- and under- dosed areas, respectively) and blurring along the direction of motion. The \(D_{\rm{ROI1}}\) and \(D_{\rm{ROI2}}\) differed depending on the motion pattern and were a function of the pk-pk amplitude. For the [20 mm-0%] pattern, the \(D_{\rm{ROI1}}\) decreased on average by 6%, the \(D_{\rm{ROI2}}\) increased by 12% compared to the stationary case (\(p\) < 0.001), and the \(H_{RMS}\) and \(GPR_{3\%/2\rm{mm}}\) decreased on average to 84% and 49%, respectively (Table 1). Only the dose distributions obtained for the [2 mm-0%] pattern met both thresholds of \(\geq\)95% (\(H_{RMS}\), \(GPR_{3\%/3\rm{mm}}\)) and \(\geq\)90% (\(GPR_{3\%/2\rm{mm}}\)) on average, but the statistical variation was up to 4% (Table 1) and some values were below the thresholds (Figure 6b). The mean irradiation time for all the sessions was found to be 2 min 47 s.  
The rescanning allowed for the partial mitigation of hot and cold spots, resulting in more uniform dose distributions (Figure 5f-h) with a mean \(H_{RMS}\) of up to 95% and 91% for the [12 mm-10%] pattern and the [20 mm-10%] pattern, respectively (Table 1). At the same time, dose blurring along the direction of motion was still observed (Figures 5h and 6h), although it also became more visually uniform (Figure 5e vs. Figure 5h). The \(D_{\rm{ROI1}}\) did not depend on the motion pattern (\(p\) = 0.81) and was on average 1.01 \(\pm\) 0.04 (mean \(\pm\) SD). However, the \(D_{\rm{ROI2}}\) for FB irradiation with rescanning was on average higher than that without rescanning (increase of 18% vs. 12% for the 20 mm pk-pk amplitude compared to the stationary case), but there was no statistically significant difference (\(p\) = 0.16). All the motion patterns used, except the [20 mm-10%] pattern, resulted in dose distributions that met the \(\geq\)95% threshold for the \(H_{RMS}\). On average, only the dose distributions obtained for the [2 mm-10%] pattern met the \(\geq\)95% and \(\geq\)90% thresholds for the \(GPR_{3\%/3\rm{mm}}\) and \(GPR_{3\%/2\rm{mm}}\), respectively. The irradiation time increased approximately 2-fold compared to FB irradiation without rescanning, averaging 6 min 9 s for all the irradiation sessions.
## 2 Materials and Methods
### Samples  
Matrix effects and interferences were evaluated using artificial and simulated body fluids (urine and blood plasma). Detailed information for the composition of each solution is given in the Supplementary Materials.  
A few mL of whole blood was donated from a group member voluntarily after informed consent was obtained. The blood sample was taken in a designated room by a trained nurse of the university hospital and was not cultured or examined in any other way than the experimental protocol presented here. After collection, red blood cells were separated from the plasma using centrifugation at 800\(\times\)_g_ for 10 min. The collected blood plasma (1000 \(\upmu\)L) was first treated with 1.0 mM TCEP for 30 min and interim vortex mixing to reduce the oxidized biothiols. Then, plasma proteins were removed by centrifugation at 12,000\(\times\)_g_ for 20 min at room temperature through centrifugal filters (MWCO = 3 kDa). This procedure was repeated twice to ensure complete removal of proteins. The collected (supernatant) liquid was diluted with distilled water (approximately 20-fold) and 1.575 mL of the sample was used for the determination of total biothiols according to the experimental procedure described above using the method of standard additions.  
Urine was also provided by a group member after informed consent was obtained. Within 30 min of collection, the urine sample was centrifuged at 6000 rpm for 20 min to remove the insoluble materials. The clarified liquid was collected and treated with TCEP as above to reduce the oxidized biothiols. Then, proteins were removed with centrifugal filters (MWCO = 3 kDa) at 12,000\(\times\)_g_ for 20 min at room temperature. The collected liquid was diluted 20-fold with distilled water and 1.575 mL of the sample was used for the determination of total biothiols according to the experimental procedure described above using the method of standard additions.  
To evaluate the accuracy of the method, known concentrations of cysteine were spiked in urine and blood plasma after protein precipitation in order to avoid potential losses during sample pre-treatment (which was based on a standard procedure and was out of the scope of this study).