esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The concept of external application of PCM layer in the building wall was presented in the paper. The general idea of this solution was to attenuate the daily temperature fluctuations on the external wall surface. Theoretically, the effect could be obtained by charging the phase change material during the day and releasing stored energy during the night. This assumption was based on preliminary, theoretical analyses conducted by authors and others, referred in Section 2.  
Based on the experiments conducted during one year of measurements it was found out that transition occurred only during some specific days. This was caused not only by the assumed melting temperature of the PCM-composite, but also by daily external temperature fluctuations. For fluctuations below 20 K, the PCM application contributed to the attenuation of temperature fluctuations of the external surface of the wall, but the efficiency of this attenuation was variable. The behavior was more regular for higher daily temperature differences.  
The assessment of PCM-composite performance and its comparison with the reference case was performed based on the Attenuation Coefficient (AC). The AC was calculated using the formula proposed by authors (when temperature fluctuations are enharmonic) as an alternative to well-known decrement factor. This original approach allowed the assessment of experimental results obtained under unstable and unpredictable changes of climatic conditions. The obtained values of AC showed that PCM-composites revealed around two times higher heat storage potential in comparison with traditional, high-density mineral wool. On the other hand, this effect strongly depended on external daily temperature fluctuations related to assumed melting temperature of PCM.  
For some, particular building applications under specific climatic conditions the stabilization of heat flux waves amplitude can lead to decreasing of heat loss and gains as well as building energy requirements for heating or cooling. The possible energy savings were not considered in that paper, but could be studied in the future works.  
This work was partly funded by The National Centre for Research and Development as part of the project entitled: 'Promoting Sustainable Approaches towards Energy Efficiency in Buildings as Tools Towards Climate Protection in German and Polish Cities: Developing facade technology for zero-emission buildings' (acronym: GPEE, project number: WPN/5/2013 (01 RS1201A)).  
[PERSON] formulated the research problem and tasks; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] built the experimental set-up, performed the experiments and collected data; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 2 Study Area and Database
### Data Base  
#### 2.2.1 Sentinel-3  
Sentinel-3 is an ocean and land mission based on a constellation of two satellites (Sentinel-3A and Sentinel-3B). Sentinel-3A was launched on 16 February 2016, with data available beginning in June 2016. It was followed by Sentinel-3B, which was launched on 25 April 2018, with data available beginning in December 2018. In this study, two years of Sentinel-3A data were used from June 2016  
Figure 2: Water bodies and DEMs with water masks (green polygons), Sentinel-3 tracks (red lines) and gauging stations (yellow location pins): (**a**) Ebro Reservoir, (**b**) Ebro Reservoir DEM, (**c**) Itoiz Reservoir, (**d**) Itoiz Reservoir DEM, (**e**) Irabia Reservoir, (**f**) Irabia Reservoir DEM, (**g**) Sotonera Reservoir, (**h**) Sotonera Reservoir DEM, (**i**) Ribarroja Reservoir and (**j**) Ribarroja Reservoir DEM.  
to June 2018. The SRAL instrument is the main topographic sensor used to provide water level measurements, and, hence, it was used in our research. The detailed parameters of the Sentinel-3 SRAL can be found in [70]. To acquire altimeter measurements, the Sentinel-3 SRAL transmits pulses at a Ku-band frequency, which is complemented by a C-band frequency to correct range delay errors due to the varying density of electrons in the ionosphere [70]. Sentinel-3 has two operational modes: SAR mode and LRM. As the SAR mode is available globally, we could retrieve inland water levels over any area tracked by Sentinel-3. The SRAL tracks the surface in two different tracking modes, namely, closed loop and open loop tracking. For closed loop tracking, the altimeter range window is autonomously positioned based on an on-board near real-time (NRT) analysis of the previous SRAL waveform; in contrast, for open loop tracking, the altimeter range window is positioned using a priori knowledge of the surface height stored in the instrument in a one-dimensional along-track DEM. The tracking modes for the studied water bodies over the Ebro River basin are listed in Table 2.  
Three levels of processed altimeter data are available: Level-0, Level-1 and Level-2 products. In our study, Level-1 non-time critical (NTC) 20 Hz data were used for the water level retrieval by the three retrackers (i.e., the threshold, OCOG and two-step physical-based retrackers), and Sentinel-3 Level-2 ocean retracked data from ESA were used for comparison.  
The main objectives of the Level-2 processing of SAR mode data are to provide elementary retracked altimeter estimates of the oceans, coastal zones, ice sheets and sea ice elevations [71]. Different retracking algorithms are more suited to specific surfaces, that is, for ocean retracking, OCOG retracking, ice sheet retracking, ice retracking and sea ice retracking. Unfortunately, ice-related retracker results are not available for inland water bodies; hence, we used Sentinel-3 Level-2 ocean retracker results for comparison. Ocean retrackers try to fit theoretically modeled multi-look Level-1B (L1B) waveforms to real L1B SAR waveforms, thereby providing estimates of the epoch, composite sigma, amplitude and the mispointing angle [71].
## 6 Experimental Validation
### System Robustness Evaluation  
To evaluate the performance and robustness of our system, we investigate the recognition accuracy under different signal-to-noise ratio (SNR) conditions. Considering that it is difficult to accurately control the SNR of the input signal over the optical path, the experiment is conducted between the PC and STM32 through the serial port. During the test, two English texts containing 2196 characters were selected as the raw data, and each character appeared randomly. Note that the transmitting data volume is far more than that of usual use in our considered real maritime scenario.  
The specific test procedure is as follows: First, the raw data of length 2196 characters are Morse-encoded and superimposed with noise in MATLAB. By controlling the proportion of noise samples, the Morse-encoded signal data with different SNRs (-3-6 dB) is generated on the PC and  
Figure 12: Simulation results of Morse signal interpreting \"hello world.\"  
Figure 13: Real-time optical Morse code recognition results.  
then transmitted to the STM32-embedded system through the serial port; secondly, STM32 stores and decodes the received data and returns the recognition results back to the host computer. Finally, in the host computer, the original and decoded data file are examined, and the decoding accuracy is statistically analyzed. The statistical decoding results during the experiment are listed in Table 2.  
The curve of recognition accuracy versus SNR derived from Table 2 is plotted in Figure 14. It is noticed that the decoding accuracy increases with the growth of the SNR. Our proposed approach can achieve an average automatic recognition accuracy of more than 90% when the SNR of the input signal is greater than \(-3\) dB. Furthermore, an accuracy of over 95% is observed with an SNR greater than 3 dB. In the case that the noise is relatively strong with an SNR of \(-3\) dB, the system can still achieve a good accuracy of 90.1%. Thus, we conclude that, in the presence of ambient light noise, the proposed mk-means based recognition system still performs well in terms of the decoding accuracy performance.
# A Method of Intelligent Driving-Style Recognition Using Natural Driving Data
## 4 Result
### Data Modeling
#### 4.3.1 Clustering Model of Driving Style Based on K-Means Algorithm
Figure 9: Radar chart of clustering results (main 3 principal components): (**a**) car-following state, (**b**) lane-changing state. dinal speed change factor. Table 5 is the statistical value of data characteristics before the dimensionality reduction of the lane-changing clustering results. Among the 985 car-following groups and 231 lane-changing groups, there are 134 vehicles with the same ID. In other words, there are 134 cars not only following cars, but also changing lanes, and there are many times these actions by the same car. Therefore, based on individual state clustering, this paper divides the comprehensive driving styles, and obtains evaluation results, as shown in Figure 10.  
Figure 10: Evaluation results of driver’s driving style. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Result** & **Conservative (Type 3)** & **Moderate (Type 2)** & **Radical (Type 1)** \\ \hline Maximum speed & 19.563 & 39.878 & 40.554 \\ Mean speed & 14.483 & 32.121 & 29.854 \\ Standard deviation of speed & 3.237 & 4.928 & 5.944 \\ Maximum absolute value of acceleration & 2.683 & 2.891 & 3.364 \\ Mean value of acceleration absolute value & 0.467 & 0.688 & 0.956 \\ Standard deviation of absolute acceleration value & 0.66 & 0.740 & 0.903 \\ Maximum acceleration & 2.452 & 2.647 & 3.165 \\ Average acceleration & 0.519 & 0.735 & 1.008 \\ Maximum deceleration & \(-\)2.126 & \(-\)2.439 & \(-\)2.899 \\ Average deceleration & \(-\)0.473 & \(-\)0.627 & \(-\)0.889 \\ Average value of positive jerk & 2.022 & 2.862 & 3.782 \\ Average value of negative jerk & \(-\)2.109 & \(-\)3.025 & \(-\)3.82 \\ Maximum positive jerk & 8.395 & 10.065 & 12.085 \\ Maximum negative jerk & \(-\)8.633 & \(-\)10.807 & \(-\)13.215 \\ Maximum lateral speed & 0.943 & 1.104 & 1.382 \\ Mean lateral speed & 0.278 & 0.271 & 0.277 \\ Standard deviation of lateral speed & 0.317 & 0.360 & 0.44 \\ Lane-changing time & 61.904 & 51.607 & 44.222 \\ Distance traveled during lane-changing time & 24.6 & 46.569 & 37.093 \\ Maximum heading angle & 0.254 & 0.125 & 0.198 \\ Interchangeable gap of target lane & 15.046 & 20.296 & 20.748 \\ Mean value of space headway before lane-changing & 15.659 & 19.421 & 17.286 \\ Mean value of time headway before lane-changing & 6.086 & 2.495 & 2.788 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Clustering results of lane-changing characteristic parameters. Compared with the driving styles in the car-following and lane-changing state, it can be seen that the comprehensive driving evaluation results obtained by the proposed method are relatively reasonable.
## 4 Performance Evaluation: Results
### Real Data-Linear Conversion  
Figures 11a-13a present results obtained with real data when a linear conversion from latency to reward is adopted. Results show that MLI obtains the worst performance with all the considered \(T_{U}/T_{M}\) ratios, with a large gap in terms of regret with respect to the other algorithms.  
This outcome can be explained by observing that the rewards obtained by linear conversion from the latencies present a large variance; in fact, every Internet web-site home page visited (corresponding to a different wireless network, in the considered scenario) answered with extremely variable latency values. This variability prevents the algorithms to correctly estimate their mean values with few samples. This is particularly true for MLI, which strongly relies for its _use_ actions choices on the estimates built up during the first steps. This is an advantage in scenarios where the measured data do not present such a high variability, as seen in Section 4.2, but leads to a significant performance penalty in the opposite case. Overall, POKER and \(\epsilon\)-decreasing are the algorithms that provide the most reliable performance, leading to the best results for \(T_{U}/T_{M}=5\) and \(T_{U}/T_{M}=10\), respectively.  
Figure 11: Performance in terms of regret of the six considered algorithms, with real captured data used as reward and \(T_{U}/T_{M}=1\). (**a**) linear conversion; (**b**) logarithmic conversion.  
Figure 12: Performance in terms of regret of the six considered algorithms, with real captured data used as reward and \(T_{U}/T_{M}=5\). (**a**) linear conversion; (**b**) logarithmic conversion.
## 3 Results  
The aforementioned simulations demonstrate the capability of the proposed ARISE control system for a switched dynamic system. Figure 2 depicts the tracking errors of the ARISE and SM controllers for a single set of dynamic parameters and disturbance inputs, highlighting the different tracking performance between the two controllers. As can be seen in Figure 2, the ARISE controller demonstrates a better tracking performance compared to the SM controller. A more thorough analysis of this performance difference is presented in Section 4. The results in Figure 2 are representative across all dynamic parameter sets and disturbances that were considered. Likewise, the RMS tracking errors for both of the controllers were analyzed across varying sets of continuous disturbances (see Table 1), and the results for a single set of dynamic parameters are presented in Figure 3.  
Correspondingly, Figure 4 presents the RMS tracking errors for both controllers under different control effectiveness values (i.e., 'GA' and 'GB') and a single set of dynamic parameters and disturbances. The RMS errors of the system once it reaches a steady state (SS) are also illustrated in Figure 4. Moreover, Figure 5 depicts the RMS tracking errors for both controllers when varying the parameter \(\mu\).  
Figure 3: RMS tracking errors across varying continuous disturbances when \(\mu=1.5\), \(c_{1}=5\), \(c_{2}=10\), \(c_{3}=3\), and \(c_{4}=6\) (i.e., set ‘GA’). The terms ‘a’–d’ refer to the disturbance configurations in Table 1.  
Figure 4: RMS tracking errors across varying dynamic parameters when \(\mu=1.5\), \(d_{1}=0.5\cos(t)\), and \(d_{2}=0.5\sin(t)\) (i.e., set ‘a’). The RMS errors of the system after steady state (SS) were also evaluated. The state of the system after 10 s is considered as SS. The ‘GA’ dynamic parameters are \(c_{1}=5\), \(c_{2}=10\), \(c_{3}=3\), and \(c_{4}=6\), and the ‘GB’ dynamic parameters are \(c_{1}=7\), \(c_{2}=10\), \(c_{3}=3\), and \(c_{4}=4\).  
Figure 5: RMS tracking errors across varying values for the dynamic parameter \(\mu\) when \(c_{1}=7\), \(c_{2}=10\), \(c_{3}=3\), \(c_{4}=4\) (i.e., set ‘GB’), \(d_{1}=0.5\cos(t)\), and \(d_{2}=0.5\sin(t)\) (i.e., set ‘a’).
###### Abstract  
The rapid growth of power grid capacity and the widespread use of a large number of power electronics and non-linear loads have led to harmonics in the power system. Harmonics in the power system will cause safety hazards to the normal operation of power equipment, exacerbate the aging of insulation materials, and reducing the overall operation reliability of the system. In the present work, we used power frequency ac voltage superimposed harmonics to carry out ageing experiments on power cable terminals. Then, we tested the infrared spectra, dielectric spectra, electrical conductivity, and surface potential decay characteristics of silicone rubber insulation materials on the cable terminals aged for different times. The experimental results show that the dielectric constant and dielectric loss of silicone rubber gradually increase with the aging time. In particular, the dielectric loss of silicone rubber changed greatly at low frequencies. The effect of dc conductance of aged silicone rubber on dielectric loss is significantly enhanced at low frequencies, which causes the dielectric loss to increase as the frequency decreases following an inverse power law. The surface potential decay rates of silicone rubber insulation after positive and negative corona charging accelerate with increasing the aging time, which is consistent with the experimental results of electrical conductivity. By analyzing the distribution characteristics of electron and hole traps in silicone rubbers, it is found that the trap energy levels of electron and hole traps become shallower as the operating time increases. The calculation of the carrier hopping conduction model shows that the shallow trap formed with increasing the aging time will lead to increases in both carrier mobility and conductivity. When the conductivity rises to a certain value, the silicone rubber will lose its insulation performance, resulting in insulation failure.  
a pacs: 1
# Performance Prediction and Optimization of Multi-Mirror Combined Solar Dish Collector
## 3 Model Validation and Analysis Optimization
### Optimization of Design Parameters for Combined Dish System  
According to the analyses above, to maximize the net thermal efficiency of the combined system, the receiver radius and system focal length can be optimized using the present calculation method. Considering whether the focal length of each mirror is uniform, the spot on the receiver is altered by the change of the mirror position, resulting in a greater loss. Therefore, the system is firstly optimized on the basis of the case that the focal length of each mirror is always equal to the distance from the mirror center to the receiver. When the optical error is fixed, the optimal net thermal efficiency of the system can be maximized by optimizing the rim angle, the receiver radius, and system focal length. In this paper, the size of the reflected mirrors of the multi-dish system was fixed; hence, the rim angle was determined by the system focal length. Therefore, only the receiver radius and system focal length needed to be optimized. As shown in Figure 9, when the optical error was 2 mrad, the net thermal efficiency varied with the system focal length and receiver radius. It can be illustrated that the optimized receiver radius was 0.1 m and the best system focal length was 7 m (Figure 9). The optimal net thermal efficiency was 86.05%, and the system intercept factor was 98.81%. Thus, the optimal system focal length and receiver radius under different optical errors could be obtained by maximizing the net thermal efficiency of the system, as listed in Table 2. From column 4 in Table 2, it can be seen that the optimal net thermal efficiency of the system decreased with the increase in optical error.  
Figure 9: Variation of the net thermal efficiency with receiver radius r and system focal length f for a multi-dish collector; loss rate = 1.67 \(\times\) 10\({}^{3}\) W/m\({}^{2}\), absorptivity = 0.98, CSR = 0.05, optical error = 2 mrad, and \(\rm f_{x}=f_{p}\).  
Usually, in order to save cost, all mirrors of the combined dish system are considered with a uniform mirror focal length. Therefore, while the optimal receiver radius and system focal length are being determined, the intercept factor of the system can be studied by the change in uniform mirror focal length to obtain the optimal value of mirror focal length. In the case of an optical error of 2 mrad, the net thermal efficiency of the system is reduced to 85.87%, with an optimal uniform mirror focal length of 8 m and a concentration ratio of about 2000. As shown in column 3 of Table 3, different specular focal lengths are listed under different optical errors. In this case, the annual net thermal efficiency changed from 0.8620 at 1 mrad to 0.8479 at 5 mrad. Certainly, comparing the net thermal efficiency in Tables 2 and 3, it can be seen that the net thermal efficiency value of the system with a uniform focal length was lower than that of the optimal mirror focal length under the same optical error.
## 3 Coverage Calculation Based on the Line of Sight
### Construction of \"Lines of Sight\" for Viewshed Analysis  
The observer point, which corresponds to the position of the camera, is characterized by its 2D coordinates (x, y) and its elevation (z). The spatial position of cameras may be determined by using a Digital Surface Model (or Digital Elevation Model) that represents the environment where the deployment will take place. In ArcGIS 10.1, which is a software designed by ESRI company, the Viewshed Analysis based on the line of sight is characterized by the following items: (1) Camera  
Figure 1: Coverage areas generated by a set of sensors.  
Figure 2: Visibility based on Line of Sight. The target T1 is visible from O but the target T2 is not visible from O (Gray line: Visible, dashed line: Invisible).  
elevation value (SPOT), (2) vertical offsets (OFFSETA, OFFSETB), (3) horizontal scanning angles (AZIMUTH1, AZIMUTH2), (4) vertical scanning angles (VERT1, VERT2), and (5) scanning distances (RADIUS1, RADIUS2) (ArcGIS Help, 10.1). Figure 3 illustrates the nine parameters that are used to perform the viewshed analysis in ArcGIS 10.1.  
Here are the explanations of the nine parameters used for controlling the viewshed analysis (ArcGIS Help, 10.1):  
* **SPOT**: Corresponds to the ground elevation for the observer point (_i.e.,_ camera).
* **OF1** and **OF2**: These two parameters define the vertical elevation to be added to the ground elevation of the observer **OF1** (OFFSETA) and the target **OF2** (OFFSETB).
* **AZ1** and **AZ2**: These two values allow defining the range of the horizontal angle that characterizes the scan from the observer (_i.e.,_ camera). **AZ1** (AZIMUTH1) corresponds to the starting angle of the scan range and **AZ2** (AZIMUTH2) corresponds to the ending angle of the scan range. Note that the values of **AZ1** and **AZ2** may range from 0 to 360\({}^{\circ}\) where 0 is defined by the north direction.
* **V1** and **V2**: These two elements delimit the vertical range of the scan from the observer. **V1** (VERT1) defines the upper limit of the vertical angle and **V2** (VERT2) defines the lower limit of the vertical angle. Note that **V1** and **V2** may vary from -90\({}^{\circ}\) to 90\({}^{\circ}\).
* **R1** and **R2**: These parameters determine the distance that may be covered by the observer (_i.e.,_ camera). **R1** (RADIUS1) corresponds to the starting distance from which a target may be visible (the points that are closer than **R1** are surely not visible). **R2** (RADIUS2) corresponds to the ending distance where any point beyond this distance will be surely not visible.