esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Review and Comparison of Interferometric Coherence and Intensity Correlation
### SAR Amplitude Data with Focus on Intensity Correlation
Furthermore, it was shown that the highest accuracy was achieved at damage assessment procedures, which combine both SAR backscatter analysis and intensity correlation (compared to the application of only one of these approaches). All reviewed damage assessment methodologies reported only a qualitative validation based on comparison of their damage assessment results with (I) high spatial resolution optical imagery, (II) damage maps derived from optical data and (III) information from field survey. However, no quantitative accuracy assessment is reported. The most detailed validation data is (III) field survey information. Unfortunately, such information is not always available. Moreover, procedures using the SAR intensity correlation can also be successfully applied to imaging constellations which do not completely fulfill the requirements of the damage assessment procedures which are based on the coherence (reviewed in Section 3.1), _i.e._, the SAR intensity correlation can also be applied at larger temporal and spatial baselines [102]. In addition, at small SARwavelength (such as X-band) areas fully or partly covered with vegetation (e.g., discontinuous urban fabric) show strong de-correlation within a short time frame, which reduces the applicability of coherence based damage assessment for SAR missions of longer repeat cycles [118] (_cf._ Section 4).
# Two-Dimensional Digital Beam Steering Based on Liquid Crystal Phase Gratings
## 4 Conclusions  
A 2D beam steering device has been demonstrated using passive electrodes and a novel electronic driver. An efficiency map of the device has been measured, where it is possible to study how the topology of the grating affects not only the deviation angle, but also the amount of light that is deviated in one direction. The use of a DLW technique to create the electrode pattern improves the efficiency, leading to a device with high fill factor. The phase variation of the interpixel gap has proven to be negligible in this device, possibly due to the small interpixel size. The maximum diagonal steering range for the binary pattern (16% diffraction efficiency) ranges from \(-\)1.65 to +1.65 degrees, and may be varied in steps of 0.005 degrees.  
Conceptualization, M.G.d.B. and M.A.G.; methodology, M.G.d.B., M.A.G., and X.Q.A.; software, M.G.d.B. and M.A.G.; validation, M.G.d.B., M.A.G., and X.Q.A.; investigation, M.G.d.B. and M.A.G.; writing--original draft preparation, M.G.d.B. and M.A.G.; writing--review and editing, J.M.O.; supervision, M.A.G. and X.Q.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministerio de Economia y Competitividad through \"Programa RETOS\" (\"Subtopic\"--TEC2016-77242-C3-2-R) and by the EU structural funds (FEDER) and the Communidad de Madrid through the \"Programa de Actividades de l + D\" (\"SINFOTON2-CM\"--S2018/NMT-4326) and the \"Programa de Doctores Industriales\" (IND2017/TIC-7835).  
Not applicable.
## 3 LE Identification Method Considering the Fluctuation of Wind Power Output
### Calculation Method of APEV Considering the Output Fluctuation of the Wind Farm
#### 3.3.3 Fast Calculation Method of APEV  
According to the above Section 3.3.2, when branch \(l\) is removed, and the change value in active power output at bus node \(i\) is \(\Delta\mathrm{G}_{i}\), the APEV of branch \(k\) consists of two parts. Among them, \(\mathrm{P}_{k}^{l}\) is a fixed value, essentially the APEV calculated by the conventional method after branch \(l\) is removed. \(G_{k-i}^{l}\Delta\mathrm{G}_{i}\) is related to the active power output of the bus nodes; when the active power output of multiple bus nodes changes, the APCV of the branch \(k\) caused by different bus nodes is independent and can be calculated in parallel.  
Therefore, in the scenario of multiple bus nodes containing wind farms, the change value brought by each node can be calculated in parallel, and the total APCV brought by all nodes can be obtained by superposition. When there are \(c\) bus nodes containing wind farms in the power system, after branch \(l\) is removed, the APEV of line \(k\) can be directly calculated using Equation (17). The calculation is very fast and meets the requirements of online calculation.  
\[\mathrm{P}_{k}^{l}=\mathrm{P}_{k}^{l,(c)}=\mathrm{P}_{k}^{l}+\sum_{i=1}^{c}G_{ k-i}^{l}\Delta\mathrm{G}_{i} \tag{17}\]  
where \(\mathrm{P}_{k}^{l,(c)}\) is the APEV of branch \(k\) for the scenario where branch \(l\) is removed and there are \(c\) bus nodes containing wind farms. The APCV of the branch caused by the active power output change in the bus nodes containing the wind farm before and after the faulty branch removal is considered, thus ensuring that the LE identification criterion described in Section 3.1 is still applicable under high penetration of wind power generation.  
Therefore, based on generalized sensitivity, a fast calculation of active power can be achieved, which is suitable for power systems with multiple nodes containing wind farms and is independent of wind power penetration. The fast calculation can avoid iterations and increase the calculation speed. After the branch is removed, according to the data uploaded by WAMS, \(\Delta\mathrm{G}_{i}\) can be obtained, which corresponds to the APCV of the wind power output before and after the removal of the faulty branch. Therefore, using Equation (17), the APEV of branch \(k\), which considers the fluctuation of the wind farm output, can be obtained after the faulty branch is removed. The overall steps in the derivation of the mathematical expression for APEV are shown in Figure 4.  
The APEV is used in the LE identification criterion described in Section 3.1 to determine the cause of branch overload. Differing from the traditional method that assumes the active power output of bus nodes to be unchanged before and after the faulty line is removed, the calculation method of the APEV considering the output fluctuation of wind farms proposed in this section can be used for LE identification in high-penetration scenarios of wind power.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 5 Experimental Results and Discussions
### Data Acquisition and Data Transmission

Table 4: The useful sampling rate of the XBeats prototype over various operation modes. We noticed some noise and outliers in the ECG singles acquired by our ECG patch prototype, as shown in Figure 6b,c, while operating in the \"continuous mode\" and \"triggered mode\". We found that the primary source of noise and outliers is a result of sudden movements and motion artifacts on the wires connecting the electrodes to the ECG patch prototype.
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 4 Results
### The Orbit Overlaps and Comparison with WUM Orbits
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**PRN**} & \multicolumn{4}{c}{**Overlaps**} & \multicolumn{4}{c}{**Vs WUM**} \\ \cline{2-9}  & **3-Day** & **5-Day** & **7-Day** & **9-Day** & **3-Day** & **5-Day** & **7-Day** & **9-Day** \\ \hline
**C01** & 19.8\% & 22.1\% & 29.1\% & 37.2\% & 12.7\% & 8.7\% & 5.8\% & 8.1\% \\
**C08** & 9.3\% & 22.1\% & 52.3\% & 62.2\% & 9.8\% & 22.5\% & 45.1\% & 55.5\% \\
**C10** & 7.0\% & 11.0\% & 27.3\% & 45.3\% & 5.8\% & 9.8\% & 30.1\% & 46.2\% \\
**C13** & 12.2\% & 44.2\% & 75.6\% & 89.0\% & 15.6\% & 50.3\% & 76.3\% & 85.5\% \\
**C11** & 43.0\% & 77.9\% & 93.0\% & 99.4\% & 53.2\% & 82.1\% & 95.4\% & 100\% \\
**C20** & 11.6\% & 43.0\% & 54.7\% & 58.7\% & 15.0\% & 41.6\% & 50.9\% & 56.1\% \\
**C21** & 27.9\% & 60.5\% & 76.2\% & 80.8\% & 36.4\% & 57.8\% & 70.5\% & 71.7\% \\
**C29** & 23.8\% & 52.9\% & 75.6\% & 82.0\% & 28.9\% & 54.3\% & 73.4\% & 76.3\% \\
**C30** & 20.9\% & 51.7\% & 65.1\% & 72.1\% & 22.5\% & 45.7\% & 59.5\% & 63.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The success rate of multi-day solutions. Figure 6: The results of SLR-only orbit determination for C01. Figure 7: The results of SLR-only orbit determination for C08. satellites can be improved from about 20 cm to better than 10 cm in R direction, and from sub-metre level to about 50 cm in both the T and N directions. Figure 11: The results of SLR-only orbit determination for C20. Figure 10: The results of SLR-only orbit determination for C11. In addition, the dependency of the accuracy of the SLR-only determination on the Sun elevation angle of \(\left|\beta\right|\) is found for BDS satellites, especially for BDS-2 C11. As shown in Figure 10, the RMS values of SLR-only orbit determination get larger when the \(\left|\beta\right|\) is near zero for both the orbit overlaps and the comparison with WUM orbits. Because it happened during the eclipse and YM period of C11, it is probably caused by the deficiency of the SRP model and attitude model on the SLR-only orbit  
Figure 12: The results of SLR-only orbit determination for C21. Figure 13: The results of SLR-only orbit determination for C29. determination. While this phenomenon is not obvious for the BDS-3 C20 and C21 at present, which may be related to the attitude control modes of BDS-3, this dependency does not necessarily exist for BDS C01, C08, C10, C13, C29, and C30 at present on account of insufficient RMS values.
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
## 5 Conclusions  
In this paper, we proposed a novel buffering scheme to adaptively adjust the ratio of caching and prefetching areas in the buffer of each peer by considering the characteristics of P2P live streaming systems, where peers buffer only a small amount of data that are distributed widely. In our proposed scheme, the buffer ratio of each peer is determined according to the relative position of its playback time in a group after forming groups of peers according to the time periods of their playback positions. We increase the ratio of the caching area in the buffer of a peer as its playback time within a group moves earlier and increase the ratio of the prefetching area as the position moves later. We also showed that the adaptive buffering scheme can achieve considerable improvement in startup delay, average jitter ratio, and ratio of necessary chunks in a buffermap by increasing data duplication among peers.  
Figure 11: Ratio of necessary chunks in a buffermap according to the number of chunks per buffermap (number of peers: 1200).  
Figure 10: Ratio of necessary chunks in a buffermap according to the number of peers (number of chunks per buffermap: 512).  
**Acknowledgments:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1A09917396) and by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8501-16-1006) supervised by the IITP (Institute for Information & communications Technology Promotion).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the simulations; [PERSON] performed the simulations; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Validation
### Experiment  
In addition to the verification cases procured from the previous studies, an experimental study available in the literature is repeated, and a novel benchmark experiment is designed in the context of this study. In both experiments, the same cable was used which is specifically designed to validate the particle-based methods. In the design of the experimental setup, spherical weights were placed throughout the cable with the same intervals. The distance between each spherical weight was designed as 2.6 mm to satisfy the assumption that adjacent spherical weights do not contact with each other during the dynamic motion of the cable. Center-to-center distance between adjacent spherical weights  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline Total cable length & 44.304 m \\ Elastic modulus & 200 GPa \\ Cross-sectional area & 2348 mm\({}^{2}\) \\ Mass per length & 20.3 kg/m \\ Additional mass & 100 kg \\ \hline \end{tabular}
\end{table}
Table 3: Parameters used in case 2.  
Figure 4: Displacement in y direction at the midpoint of a suspended catenary cable in Case 2.  
Figure 3: Model of case 2.  
was 10.6 mm and each sphere had a mass of 0.285 g including the mass of cable. Thus, the cable model had a 0.2656 N/m self-weight. Bending stiffness of the cable was assumed to be zero. A uniformly loaded cable model designed in accordance with the particle-based models was achieved. Tensile modulus of the cable was recorded as 109 GPa.  
A high-definition camera was used to digitize the motion of the cable with the help of an image processing technique. The camera can capture 60 frames per second with 720 pixels. Some intermediate spherical weights on the cable were marked and their motions were recorded and digitized.
# SVIoT: A Secure Visual-IoT Framework for Smart Healthcare
## 3 Proposed Framework: SVIoT  
In a smart healthcare-based setup, IoT nodes are deployed for the transmission of acquired data to a remote doctor. The transmission of this data over insecure communication networks can bring forth security, privacy, and content integrity issues. In such a scenario, the development of novel security mechanisms for IoT-driven networks is necessary. Towards this end, we developed a novel security mechanism, called SVIoT, for the transmission of patient information in an IoT-based healthcare setup. It requires relatively reduced resources, such as memory and bandwidth, which is in tune with IoT-setup requirements. The main purpose of the SVIoT is to ensure reversibility, privacy, and data integrity, by hiding the sensitive data in a cover image before it is transmitted through an insecure network in the IoT setup. The block diagram of the proposed SVIoT scheme is shown in Figure 2. Firstly, Visual-IoT sensors are used to collect the information (image data). This image is then used to carry patient information, comprised of details including blood pressure, heart rate, ECG, etc., obtained by a set of IoT sensors. The patient's personal information, such as patient name, age, sex, ID, insurance, etc., is appended to it. This information capsule of details comprises our secret data. The secret data are first encrypted, using an AES-128 encryption algorithm, and is then embedded in the patient's medical images. This is followed by embedding it in the cover image generated by ODNMI. Section 3.1 describes ODNMI in detail. To ensure better imperceptibility of watermarked images, we make use of LDS on the data to be embedded. Section 3.2 provides a brief description of LDS. Data embedding is discussed in Section 3.3, while data extraction is presented in Section 3.4.
## 4 Results
### Experiments with RSR-Part II  
The results obtained with Part II are shown in Table 3. In this set of experiments, the phrases are shorter so we can see that we need fewer states to model them. However, the performance obtained with models of 10 and 20 states is similar since some phrases might require more states than others. For example, phrases like _\"Turn on coffee machine\"_ is modelled better with 20 states, while others like _\"Call sister\"_ need only a 10 state model to be characterized.  
Furthermore, as we expected, the general performance is worse since the system suffers from the lexical similarity of the short commands. Thus, this part is more challenging than Part I as we can also see in other previous works [15; 16].  
In addition to the previous table, Figure 7 depicts the Detection Error Trade-off (DET) curves. These representations show the performance for female+male experiments with the best systems for each pooling configuration. We can observe that the results with the HMMs of 10 or 20 states are practically the same.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**Architecture**} & \multicolumn{3}{c}{**Results (EER\%/DCF10)**} \\ \hline \multicolumn{3}{c}{**Front-End**} & \multicolumn{3}{c}{**Pooling**} \\
**Layers** & **Kernel** & **Type** & **States** & **Fem** & **Male** & **Fem+Male** \\ \hline
3C & 3 & _arg_ & – & 11.18/0.9824 & 11.81/0.9811 & 11.59/0.9813 \\ \hline – & – & _HMM_ & 10 & 7.99/0.7062 & 9.47/0.6741 & 9.79/0.7810 \\
1C & 1 & & & 4.76/0.5901 & 4.70/0.5559 & 5.47/0.6488 \\
1C & 3 & & & 4.16/0.5466 & **4.31/0.5387** & 4.94/0.6140 \\
3C & 3 & & & 3.91/0.5398 & 4.97/0.6536 & 4.68/0.6151 \\
4C & 3 & & & 4.82/0.6421 & 6.13/0.7092 & 5.73/0.6876 \\ \hline – & – & _HMM_ & 20 & 10.06/0.7449 & 10.01/0.7311 & 11.06/0.8032 \\
1C & 1 & & & 6.44/0.6709 & 5.56/0.6012 & 7.02/0.7403 \\
1C & 3 & & & 5.57/0.6178 & 5.01/0.5547 & 6.17/0.6865 \\
3C & 3 & & & **3.90**/0.5420 & 4.86/0.6451 & **4.61/0.6607** \\
4C & 3 & & & 5.00/0.6539 & 6.23/0.7213 & 5.88/0.6960 \\ \hline – & – & _HMM_ & 40 & 18.13/0.8197 & 18.16/0.8146 & 18.43/0.8563 \\
1C & 1 & & & 11.60/0.7995 & 10.53/0.7493 & 11.74/0.8469 \\
1C & 3 & & & 9.52/0.7689 & 8.83/0.7039 & 9.95/0.8190 \\
3C & 3 & & & 5.55/0.6461 & 6.76/0.6830 & 6.59/0.6794 \\
4C & 3 & & & 7.56/0.7607 & 9.52/0.8147 & 8.93/0.7943 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results on RSR2015-Part II [22] eval set, showing EER% and NIST 2010 min cost (_DCF10_). These results were obtained by training with bkg+dev subsets and by varying the number of states of the HMM. The best results are marked in bold.  
Figure 7: Detection Error Trade-off (DET) curve for female+male results on RSR2015-Part II of the best systems for each pooling configuration.
## 2 Dynamic Modeling of Spalling Faults at Non-Central Positions of the Inner-Ring Raceway
### Rolling Element-Raceway Contact Area  
The actual contact position is within the contact elliptical area. As shown in Figure 4(a), if the spalling fault occurs outside the contact elliptical surface, it will not have an impact on the bearing. As shown in Figure 4(b), if the spalling fault occurs at the circle center of the contact elliptical surface, a single radial impact will be generated. As shown in Figure 4(c), if the spalling fault occurs at a non-central position of the contact elliptical surface, a radial impact and a derived axial impact will be generated.  
Figure 4: Circle center trajectory.  
Figure 5: Spalling fault-contact ellipse position.  
It can be seen from this that the location of the spalling defect must be within the contact stress area; otherwise, it will not cause the bearing to vibrate. As shown in Figure 6, the boundary condition of the axial position angle \(\tau\) of the spalling fault is:  
\[\tau\leq\arcsin\frac{a}{r_{b}} \tag{12}\]  
where, \(r_{b}\) is the radius of the rolling element, and the formula for calculating the major axis 2a of the contact-area ellipse is:  
\[2a=\mu\left\{\frac{24\left(1-\frac{1}{m^{2}}\right)Q}{E\sum\rho}\right\}^{ \frac{1}{3}} \tag{13}\]  
The formula for calculating the minor axis 2b of the contact area ellipse is:  
\[2b=\
u\left\{\frac{24\left(1-\frac{1}{m^{2}}\right)Q}{E\sum\rho}\right\}^{ \frac{1}{3}} \tag{14}\]  
According to the geometric relationship between the rolling element and the raceway, the axial pressure in the contact elliptical area is the same, and the radial direction is affected by the curvature of the inner ring, with the edge pressure being small and the middle pressure being the maximum. The contact stress \(P_{\max}\) of the elliptical area is calculated by Hertz contact theory, and the formula is:  
\[P_{\max}=\frac{1.5}{\pi}\left\{\frac{3}{E}\left(1-\frac{1}{m^{2}}\right) \right\}^{-\frac{2}{3}}\frac{1}{\mu\
u}(\sum\rho)\frac{2}{3}Q\frac{1}{3} \tag{15}\]  
where, E is the elastic modulus (for steel it is 208,000 MPa), m is the Poisson's ratio (for steel it is 10/3), Q is the load of the rolling element, and \(\sum_{\rho}\) is the sum of the principal curvatures. The calculation formula is:  
\[\sum\rho=\frac{1}{D_{w}}\left(4-\frac{1}{f}\pm\frac{2\gamma}{1\mp\gamma}\right) \tag{16}\]  
\[\gamma=\frac{d_{b}}{D_{pw}} \tag{17}\]  
Figure 6: Contact ellipse parameters of rolling body-inner race raceway.  
where, **f** represents the ratio of the raceway radius to the radius of the rolling element, and \(\mathbf{D_{pw}}\) is the pitch circle diameter.
# Mdp1 _sciences_
## 2 Methods and Data  
### Forecast-Based Curtailment (FBC)  
In the following, a brief overview of FBC method will be provided, and some new results, relevant to the present context, will be reported. For a detailed description of the analytical framework of the FBC method, the reader is referred to Reference [20]. We start our discussion by introducing the random variables  
\[X=v_{n}-v_{n-1},Y=v_{n+1}-v_{n}, \tag{1}\]  
\[X_{p}=P_{n}-P_{n-1},Y_{p}=P_{n+1}-P_{n}, \tag{2}\]  
which denote the changes in wind speed \(v\) and output power \(P\) between pairs of time intervals \(t_{n-1}\), \(t_{n}\), and \(t_{n+1}\), respectively. The FBC method focuses on short-term changes, so typically the time step \(\Delta t=t_{n}-t_{n-1}\) will be of the order of 1 to 10 min. \(t_{n-1}\) is the last time step with a measured wind speed value; time steps \(t_{n}\) and \(t_{n+1}\) lie in the future, so \(v_{n}\) and \(v_{n+1}\) have to be predicted. \(X\) and \(Y\) have an associated joint or bivariate probability density function (pdf) \(f_{XY}(x,y)\). Similarly, a bi-pdf can be defined for the changes in output power: \(f_{X_{p},Y_{p}}(x_{p},y_{p})\). As argued in Reference [20], for short time intervals (1-10 min), the correlation between consecutive changes is generally negligible, and the bivariate pdf therefore, factorizes: \(f_{XY}(x,y)=f_{X}(x)f_{Y}(y)\). For the 10-min observational data studied in this work, this assumption continues to hold to a good approximation.  
The wind speed values are converted to corresponding turbine power values by applying the turbine power curve \(P(v)\), i.e., \(P_{n}=P(v_{n})\). For the purposes of this work, it will be assumed that \(P(v)\) is a monotonically increasing function for the wind speed range \([v_{\text{cut-in}},v_{\text{rated}}]\) and constant elsewhere (\(=0\) for \(v<v_{\text{cut-in}}=P_{\text{rated}}\), for \(v>v_{\text{rated}}\)). The considerations in this paper can be generalized to stochastic power curves [25]; alternatively, power, rather than wind speed, can be used for the forecasting procedures. However, for the sake of brevity, only wind speed forecasting and monotonic power curves will be considered.  
In the following, it will be assumed that negative changes in power can be tolerated by the electricity grid only up to a critical slope value \(-\alpha=(\text{d}P/\text{d}t)_{\text{crit}}\). Events with \(\text{d}P/\text{d}t>-\alpha\) will be referred to as (slope) _compliant_. For the purposes of the discrete formulation, it is convenient to define the positive quantity \(a_{p}=\alpha\Delta t>0\). The variable of primary interest is the power change in the second time interval, i.e., \(y_{p}\). If \(y_{p}<-a_{p}\)the wind farm or turbine is said to be incompliant; however, often, this incompliance can be avoided by a slight reduction (curtailment) of the output power at \(t_{n}\), resulting in a less negative power change \(y_{p}\) in the second time interval. Figure 1 shows the general idea of the FBC method.