esa-sceva/satcom-chunk-collection huggingface dataset:
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 7 Future Scope  
In the future, we want to use this approach to design efficient medium-range communication (MAC) protocols, especially for heterogeneous networks, to prevent lengthy delays and broaden simulation scenarios. In the future, we will focus on establishing a routing protocol to make the network more cost-effective while also minimizing the end-to-end delay, which we plan to deploy shortly. Furthermore, integrating encryption, decryption, and blockchain models to give high-security levels to WSN encourages the development of security algorithms. The energy efficiency of the EE-SS methodology can be improved even more in the future by including more optimized data aggregation methods in its development. Additionally, techniques for allocating resources based on metaheuristic algorithms could be made to make sure that the resources are used as well as possible.  
Conceptualization: S.B. and S.S.; methodology: S.B., validation: S.B.; formal analysis: investigation: S.B. and S.S., resources: S.B., data curation: S.S. and S.B.; writing original draft preparation: S.B.; writing review and editing: S.B. and S.S.; visualization: S.B.; supervision: S.S., project administration: S.B. and S.S.; funding acquisition. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
## 4 SNR Estimation
### Time Domain SNR Estimation Using Autocorrelation
Therefore, the estimation of the SNR is given as:  
\[SNR_{MIMO-est}=\frac{P_{SS,est}}{\sigma_{N,est}^{2}} \tag{14}\]  
Subsequently, the NMSE performance of the SNR estimator can be quantified using the following equation:  
\[NMSE=\frac{1}{M}\sum_{m=0}^{M-1}(\frac{SNR_{MIMO-est}-SNR_{act}}{SNR_{act}})^{2} \tag{15}\]  
where the number of estimated instantaneous SNR at which the NMSE is quantified is denoted as \(M\) and the actual SNR is denoted as \(SNR_{act}\).
# Metasurface-Based Polarimeters
## 2 Fundamentals
### Stokes Parameters  
Before we begin reviewing the recent progress in metasurface-based polarimeters, we briefly introduce the connection between the SOP and the Stokes parameters that are usually measured in experiments [5; 67; 68]. Consider a monochromatic plane wave that propagates along the \(z\)-direction, its electric field can be described by the following Jones matrix:  
Figure 1: Schematic of a one-dimensional (1D) system with a metasurface positioned at the interface between two media.  
\[\mathbf{E}_{0}=\begin{pmatrix}A_{x}\\ A_{y}e^{i\delta}\end{pmatrix}, \tag{6}\]  
where \(A_{x}\) and \(A_{y}\) are the amplitude coefficients and \(\delta\) is the phase difference between the two components [67; 68]. Since conventional detectors only respond to the intensity of the incident light (i.e., \(I\propto A_{x}^{2}+A_{y}^{2}\)), the phase difference \(\delta\), an important parameter, is inherently difficult to probe experimentally. To remedy this shortcoming of losing the phase information, the Stokes parameters are introduced to fully describe both the amplitude and SOP of a plane wave. Typically, the Stokes parameters are determined by six intensity measurements, which can be written as:  
\[s_{0} = A_{x}^{2}+A_{y}^{2} \tag{7}\] \[s_{1} = A_{x}^{2}-A_{y}^{2}\] (8) \[s_{2} = 2A_{x}A_{y}\cos\delta=A_{a}^{2}-A_{b}^{2}\] (9) \[s_{3} = 2A_{x}A_{y}\sin\delta=A_{r}^{2}-A_{l}^{2}, \tag{10}\]  
where \(s_{0}\) is the intensity of the analyzed beam, and \(s_{1}\)-\(s_{3}\) contain information on the SOP [67; 68]. Additionally, \(s_{1}\)-\(s_{3}\) can be obtained by measuring the intensities of the two orthogonal components in the three basis sets (\(\hat{\mathbf{x}},\hat{\mathbf{y}}\)), (\(\hat{\mathbf{a}},\hat{\mathbf{b}}\))=\(\frac{1}{\sqrt{2}}(\hat{\mathbf{x}}+\hat{\mathbf{y}},-\hat{\mathbf{x}}+\hat{ \mathbf{y}})\), and (\(\hat{\mathbf{r}},\hat{\mathbf{l}}\))=\(\frac{1}{\sqrt{2}}(\hat{\mathbf{x}}+i\hat{\mathbf{y}},\hat{\mathbf{x}}-i\hat{ \mathbf{y}})\). Here, the basis (\(\hat{\mathbf{a}},\hat{\mathbf{b}}\)) corresponds to a rotation of the Cartesian coordinate system (\(\hat{\mathbf{x}},\hat{\mathbf{y}}\)) by \(45^{\circ}\) with respect to the \(x\)-axis, while (\(\hat{\mathbf{r}},\hat{\mathbf{l}}\)) is the basis for CP light. It should be noted that \(s_{1}\)-\(s_{3}\) are often normalized by \(s_{0}\) so that all possible values are within \(\pm 1\). Additionaly, one can see that \((s_{1}^{2}+s_{2}^{2}+s_{3}^{2})/s_{0}^{2}=1\), which shows that the SOPs in three-dimensional (3D) space (\(s_{1},s_{2},s_{3}\)) can be represented by the so-called Poincare sphere.
## 5 Conclusions  
To monitor the bearing health condition at early stages for taking timely maintenance actions, this study has investigated the vibration characteristics of deep groove ball bearings with consecutive increases in clearances and loads. A dynamic model is introduced to gain the vibration responses of a bearing before local defects appearing on raceways. The proposed model is verified based on the good consistency between the numerical simulation and the bearing test rig data.  
The dynamic force exhibits as a repetitive impulse train in accordance with the frequency of BPFO. The maximum magnitude shows a significant monotonic increase with clearances, which will lead to a substantial decrease, about 30%, in bearing life time.  
Moreover, vibration responses can be used to indicate the changes of clearances. Particularly, BPFO is the dominant modulation component from the envelope spectrum in both the load and non-load directions. RMS and spectral centroid values show an uptrend with the increase in clearances under different operating conditions, especially the spectral centroid of the low frequency band in the load direction, allowing the change in clearances to be monitored with good accuracy. However, the RMS value and spectral centroid show certain fluctuations with increasing bearing clearances, due to the resonance magnification in low-load conditions, which must take special care in predicting changes in clearances. Nevertheless, reducing the resonance effect and finding optimal indicators will be the future study in this subject.  
Conceptualization, F.G.; data curation, formal analysis, methodology, programming, literature review and original draft preparation, M.X.; software and validation, F.G. and M.X.; investigation, G.F and A.B.; review and editing, M.X., G.F. and Q.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Innovating major training projects of Beijing Institute of Technology, Zhuhai, XKCQ-2019-01.  
The authors gratefully acknowledge the support of the State Key Laboratory of Mechanical Transmission (SKLMT), University of Chongqing University, P.R. China and the Centre for Efficiency and Performance Engineering (CEPE), University of Huddersfield, United Kingdom.  
The authors declare no conflict of interest.
## 3 Experimental Settings
Different types of variograms (spherical, exponential, Gaussian, linear and power) were fit to the data, using autofitVariogram (from automap package) [34] and the best one (in terms of the lowest MSE) was selected. While for using the fit.variogram function (from gstat package) [35] the user has to supply an initial guess (estimate) for the sill, range, etc. to fit a certain type of variogram, the use of the autofitVariogram function (from automap package) does not necessitate any initial guess. autofitVariogram has the advantage to provide this estimate by computing it based on the data, and then calls the fit.variogram function [35]. So, autofitVariogram automatically provides fit parameters (sill, nugget and range) of the best variogram. For a deeper insight on the variogram fitting, one can see the vignettes of the mentioned packages on the website of R software [34; 35]. In our case, the fit variogram was of an exponential type, with the parameters sill = 144.9766 and range = 48,843.73. To check if the MSE obtained in OIDW (A and B scenarios) and IDW* are not statistically different, the following statistical tests have been performed:  
* The Anderson-Darling test [36], for testing the null hypothesis of data series normality: The series is Gaussian (has a normal distribution), Against the alternative that: The series is not Gaussian (has not a normal distribution). * The Levene's test [36] for verifying the null hypothesis that: The MSE series (from the scenarios A, B and IDW*) have the same variance, Against the alternative that: The MSE series do not have the same variance;
* The non-parametric Kruskal-Wallis test [37], for checking the null hypothesis that: The mean ranks of the groups of MSE series (from the scenarios A, B and IDW*) are the same. Against the alternative: The mean ranks of the groups are not the same. * The non-parametric eqdist.etest test [38], for verifying the null hypothesis that: The MSE series (from the scenarios A, B and IDW*) have the same distribution, Against the alternative: The MSE series did not have the same distribution. These nonparametric tests were selected because the normality hypothesis was rejected for some MSE series in A, B scenarios and IDW*. These tests were performed at the significance level of 5%, using the R software, version R 3.5.1.
## 3 Results
### Optimization of MAE and Verification of Predictive Model  
The optimal MAE conditions for producing a polyphenol-rich crude extract from cinnamon leaves (PCL) using MAE were predicted by maximizing the response values for yield, TPC, TF, and DPPH radical scavenging activity using Minitab 18 based on maximum desirability. The desirability value of yield, TPC, TFC, and DPPH were 0.8038, 0.8436, 0.8505, and 0.9182, respectively. According to the desirability scale, the results indicate that the desirability model falls within the range of acceptable to excellent, with values between 0.8 and 1 [25]. The optimal MAE conditions were evaluated to be an MAE time of 25 min, a microwave power of 214.24 W, and a solid-liquid ratio of 1:195.76 g/mL. The optimization provided the maximum predicted levels of 18.56% for extraction yield, 22.86 mg GAE/g for TPC, 13.89 mg QE/g for TPC, and 83.30 ug/mL for IC\({}_{50}\) of DPPH scavenging. The results of experiments conducted under optimal MAE parameters are shown in Table 4. A relative percent error of 2.30% for yield, 1.04% for TPC, 1.32% for TPC, and 0.50% for antioxidant activity was calculated, indicating the variation between the predicted and actual experimental results. The experimental results were consistent with the anticipated results, demonstrating the validity of the FCCD model's capacity to predict the presence of antioxidant phytochemicals and antioxidant activity using MAE.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Variable**} & \multicolumn{1}{c}{**Unit**} & \multicolumn{1}{c}{**Optimal**} & \multicolumn{1}{c}{**Modified Optimal**} \\  & & **Condition** & & **Condition** \\ \hline Extraction time & min & 25.00 & 25.00 \\ \hline Microwave power & W & 214.24 & 210 \\ \hline Solid–liquid ratio & g/mL & 1:195.76 & 1:196 \\ \hline \multirow{2}{*}{**Response**} & \multirow{2}{*}{**Unit**} & **Predicted** & **Predicted** & **Actual Value** \\  & & **Value** & **Value** & **Actual Value** \\ \hline Yield & \% & 18.56 & 18.58 & 19.01 \(\pm\) 0.23 \\ \hline TPC & mg GAE/g & 22.86 & 22.83 & 23.10 \(\pm\) 0.26 \\ \hline TFC & mg QE/g & 13.89 & 13.86 & 14.01 \(\pm\) 0.07 \\ \hline Antioxidant activity (IC\({}_{50}\)) & ug/mL & 83.30 & 83.13 & 82.11 \(\pm\) 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Response variable predictions under optimum MAE settings.  
The optimal MAE conditions for producing a valuable PCL yield were a 25 min of extraction time, 214.24 W microwave power, and a solid-liquid ratio of 1:195.76 g/mL. The verification of the predictive model under these conditions showed minimal relative percent errors, confirming the accuracy and reliability of the FCCD model in predicting extraction yield, antioxidant content, and activity.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 3 Determination of Embedded HVDC Operation Point
### System Total-Loss Minimization (STLM) Control
#### 3.1.4 Application of Three-Dimensional Powell's Direct Set (3-PDS)  
The operating point of the BTB VSC HVDC is found from the minimization of the total loss (Equation (3)), the voltage reference (Equation (4)), and the thermal limit (Equation (5)) as described above. In this paper, Powell Direct Set (PDS) theory was used in an optimization method to find the operating point [30]. The PDS method can directly search for optimum point, instead of using differential, and obtains the optimization point using its direction. Therefore, it is easy to apply and is not restricted by whether it can be differentiated [31; 32].  
Especially, VSC HVDC uses three-dimensional PDS because it has three controllable elements: \(\{\text{P},\;Q_{rec},Q_{rec}\}\), that are independent of each other. In order to find the three-dimensional minimum point, we tried to find the operating point decision point of HVDC more easily by using 3-PDS instead of finding the differentiation point. The main steps to determine the point of operation using the 3-PDS method are shown as follows.  
1. Set the initial point \(S_{0}^{(1)}\) and the independent directions used for initial searching \(\epsilon\). We set the values as \(S_{0}^{(1)}=(0,00)\), \(\epsilon=\{\text{ P }\rightarrow,\;\overrightarrow{Q_{rec}},\overrightarrow{Q_{inv}}\}\).  
Figure 3: Power flow change caused by back-to-back (BTB) HVDC active power operation.  
A conjugate direction should be generated within each iteration. Starting from \(S_{0}^{(k)}\)(\(k\) is the iteration number), sequentially search the directions in \(\epsilon\) by finding the minimum. \[Mint\,F_{ob}\bigg{(}S_{i}^{(k)}\bigg{)}=F_{ob}\bigg{(}S_{i-1}^{(k)}+\delta \Delta\epsilon\bigg{)},\] (10) where \(\delta\) is the step size. A conjugate direction \(\epsilon_{conju.}^{(k)}\) is generated after searching down by \[\epsilon_{conju.}^{(k)}=S_{i}^{(k)}-S_{0}^{(k)}.\] (11)
3. Update the search direction \(\epsilon\) by adding directions \(\epsilon_{conju.}^{(k)}\) to \(\epsilon\) and replace the other direction. It is common to replace the first direction with a new \(\epsilon_{conju.}^{(k)}\), but in this application, since the control of active power is much more influential than the control of reactive power in loss reduction, so the direction of active power ( \(\mathrm{P}\rightarrow\)) is left and the new direction is updated. We can get \(\epsilon=\{\mathrm{P}\rightarrow,\epsilon_{conju.}^{(k)},\epsilon_{conju.}^{( k+1)}\}\).
4. Find the new optimal operation point \(S_{0}^{(k+1)}\).
5. Convergence check.
# The 2014 Effusive Eruption at Stromboli: New Insights from In Situ and Remote-Sensing Measurements
###### Abstract  
In situ and remote-sensing measurements have been used to characterize the run-up phase and the phenomena that occurred during the August-November 2014 flank eruption at Stromboli. Data comprise videos recorded by the visible and infrared camera network, ground displacement recorded by the permanent-sited Ku-band, Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) device, seismic signals (band 0.02-10 Hz), and high-resolution Digital Elevation Models (DEMs) reconstructed based on Light Detection and Ranging (LiDAR) data and tri-stereo PLEIADES-1 imagery. This work highlights the importance of considering data from in situ sensors and remote-sensing platforms in monitoring active volcanoes. Comparison of data from live-cams, tremor amplitude, localization of Very-Long-Period (VLP) source and amplitude of explosion quakes, and ground displacements recorded by GBInSAR in the crater terrace provide information about the eruptive activity, nowcasting the shift in eruptive style of explosive to effusive. At the same time, the landslide activity during the run-up and onset phases could be forecasted and tracked using the integration of data from the GBInSAR and the seismic landslide index. Finally, the use of airborne and space-borne DEMs permitted the detection of topographic changes induced by the eruptive activity, allowing for the estimation of a total volume of 3.07 \(\pm\) 0.37 \(\times\) 10\({}^{6}\) m\({}^{3}\) of the 2014 lava flow field emplaced on the steep Sciara del Fuoco slope.  
Stromboli volcano; landslides; effusive activity; Ground-Based InSAR; infrared live cam; seismic monitoring; PLEIADES; Digital Elevation Models; optical sensors; volcano remote sensing 2018 10 2035 10.3390/rs10122035 www.mdpi.com/journal/remotesensing
## 5 The Proposed KM MCC Platform
### Mobile Application
#### 5.3.2 Offline Usage and Data Synchronization  
The mobile application is meant to be used by on-site workers (R1 and R2). The work context may change frequently due to the user's movement or changes in the features of the physical location where the user is. Hence, the mobile application must detect changes in the work context and adapt its services based on them [87]. In this regard, the most important context change for mobile construction professionals is the availability of an Internet connection, as Wi-Fi signals are usually highly unstable on construction sites and mobile Internet is not guaranteed (the worker may or may not have a data plan or broadband providers may lack coverage in specific areas).  
Therefore, the mobile application has to be able to function properly with an unstable Internet connection without sacrificing the consistency and availability of the information shared, following R4. This issue is addressed using the software design pattern called \"strategy\". Figure 9 shows the sequence diagram for the synchronization process between the mobile application and the cloud service. When the device is connected to the Internet, it uses the cloud repository, which sends information directly to the server. When it is not connected, it uses the local repository, which stores the changes locally. When the connection is restored, the local repository tries to synchronize with the cloud repository. To ensure the consistency of the information, if any irreconcilable discrepancy is detected, the user has the final decision on what version of the information should be preserved.  
Figure 8: Native, yet similar, Microblog UIs for iOS, Windows Phone, and Android.
## 2 Multiuser MIMO Configurations  
Considering the downlink of a MIMO configuration with \(M_{t}\) antennas at the transmitter and \(M_{r}\) antennas per MS (\(M_{t}\times M_{r}\) configuration), the transmitted signal can be expressed as [22]  
\[\mathbf{x}_{k}(t)=\sqrt{p_{k}}\mathbf{t}_{k}b_{k}(t)c_{k}(t) \tag{1}\]  
where \(\mathbf{x}_{k}(t)\) is the \(M_{t}\times 1\) transmission signal matrix of the \(k\)th MS (\(1\leq k\leq K\)), \(p_{k}\) is the allocated power, \(\mathbf{t}_{k}\) represents the \(M_{t}\times 1\) precoding matrix, \(b_{k}(t)\) is the transmitted symbol, while \(c_{k}(t)\) is a spreading sequence for Code Division Multiple Access (CDMA) schemes [23]. Hence, in this context, a code-domain NOMA system was considered that can support multiple transmissions within the same time-frequency resource block by assigning different codes to different MSs [24].  
The received signal at the \(s\)th symbol period (i.e., \([(s-1)\times T,\,s\times T]\) where \(T\) is the symbol duration, for simplicity time indexes have been omitted) after Maximal Ratio Combining (MRC) via 2-D space-time RAKE processing is obtained by [25]  
\[\sum_{l=1}^{L}\zeta_{k,l,s}=\sum_{l=1}^{L}\mathbf{r}_{k,l}\left(\begin{array}[] {l}\mathbf{H}_{k,l}\mathbf{t}_{k}\hat{b}_{k,s}\sqrt{p_{k}}+\\ \sum\limits_{\begin{array}{l}l^{\prime}=1\\ l^{\prime}\
eq l\end{array}}^{K}\ \
## 1 Introduction
We used RGB-LED as the transmitter and modulated each LED with a variable frequency, duty-ratio, and phase difference of three input signals of red (R), green (G), and blue (B) diodes using pulse-width modulation (PWM) method so that different LEDs would have six different features: the number of the R, G, and B image's bright stripes (3); the area of the LED image; the skewing of the R, G, and B images' stripes (1); and the ratio of the bright stripe's width to both the bright and dark stripes' width (duty-ratio of the bright stripes). Once the LED image is captured by a CMOS image sensor, an image processing method is used to extract the features of RGB-LED-ID. In order to use the extracted features to identify the LED-ID, a linear support vector machine (SVM) and a Back Propagation (BP) neural network are used. By off-line training for the classifiers and online recognition of LED-ID, the system proposed can detect and identify LED-ID well. As the experimental result shows, the proposed method could label roughly 1.05 \(\times\) 10\({}^{7}\) unique LED-ID with more than 95% recognition. The maximum distance could reach to 6 m, which is greater than the QR code and scheme in Ref. [13]. It can meet that requirement of a supplementary technique for QR codes. Besides, with the upgrading of camera technology, the number of the recognizable LEDs would also increase and LEDs with larger luminous areas can significantly increase the maximum distance. As an LED-to-camera communication technology, this is the first application of VLC in the field of IoT. The OBC we proposed can be used in many scenes. In the field of mobile payment, the security of payment can be increased. Furthermore, it is convenient to use in dark and distant conditions, reducing the queuing time at the time of payment. In the outdoor advertising field, the OBC can also be used. Limited by the size of the billboard, the product information displayed on the billboard is simple. If the OBC is used, people interested in the product can scan the billboard through the smartphone to get the details of the product. These are just two examples of OBC applications, but many more are available. The remainder of this paper is organized as follows: The background is described in Section 2. The system principle is in Section 3. Experiments and analysis are shown in Section 4. Finally, in Section 5, the conclusion is provided.