esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
## 3 Fiber-Based Photothermal Gas Sensors
### Fiber-Based Heterodyne Detection of PTS Signals  
The idea of using inexpensive and reliable fiber-based components in a PTS gas sensor was for example presented in Reference [24]. In this work, the PTS signal was registered in a Mach-Zehnder interferometer which was constructed based on telecom-wavelength (1550 nm) fiber technology. The schematic of the setup is presented in Figure 4.  
Figure 4: Schematic diagram of PAS measurement using a 2 \(\upmu\)m tunable fiber laser as the pump source, and a Mach-Zehnder fiber interferometer at 1.55 \(\upmu\)m for path-length modulation signal retrieval. TDFA—thulium doped fiber amplifier, DM—dichroic mirror, AOM—acousto-optical modulator (\(\Omega\) = 40 MHz), PD—photodiode. Reprinted with permission from [24] C The Optical Society.  
The base of the sensor was formed using a low-power singlemode 1.55 \(\upmu\)m laser diode-_probe_. The radiation of the _probe_ laser was split using a 3 dB fiber coupler to form two independent arms of the interferometer--reference and probe beams. The probe beam was launched into a 20 cm-long gas absorption cell filled with pure carbon dioxide (CO\({}_{2}\)) gas at 740 Torr pressure. The reference beam was up-shifted by 40 MHz in frequency using an acousto-optic modulator (AOM). After passing the cell, the probe beam was combined by a second coupler with the reference beam and delivered to a photodiode to create a beatnote. The AOM frequency shift of the reference beam was necessary to move the beatnote to a higher frequency, limiting the 1/_f_ noise of the sensor. In this experiment, a singlemode laser diode with emission wavelength of 1573 nm was used as a _pump_ source, targeting a weak absorption line of CO\({}_{2}\) (R-branch of the 2v1+2v2+v3 combination band of CO\({}_{2}\)). Additional erbium-doped fiber amplifier [25] boosted the _pump_ radiation output power to approximately 300 mW. The _pump_ beam was additionally modulated by a sinewave signal with frequency \(f_{m}\) and swept across the CO\({}_{2}\) absorption line using a triangular current ramp, similar to the traditional WMS gas detection technique. The effect of the PT-induced RI modulation was clearly observable as a frequency modulation of the beatnote. By incorporating an FM demodulator [26] and filtering the signal at 2\(\gamma\)_m_, the PTS signal could be resolved by techniques commonly used in WMS. This proof-of-concept PTS sensor reached an NEC of 400 ppm. Note, that the sensor had only a 20-cm-long gas-laser interaction length and a reasonable _pump_ power intensity with a relatively large beam diameter (\(\sim\)3 mm).
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 5 Simulation Results and Discussion  
The simulations performed employing MATLAB aim at: (i) simulating the transmission environment of MV power lines in the NB-PLC frequency range and (ii) studying the relevant BER performance of F-OFDM and W-OFDM signals. The motivation is to derive BER curves in an attempt to determine the maximum distance to which NB-PLC signals can be reliably transmitted via non-branched MV power lines without having to be repeated. The values of the parameters used for the simulation of the two OFDM options examined are tabulated in Table 1.
## 2 Materials and Methods
### Data Analysis  
To assess the reliability between simulated and experimental conditions, the intraclass correlation coefficient (ICC) was used. With this, 95% confidence intervals were calculated using Microsoft Excel based on an absolute agreement: specifically, a mixed-effect model (ICC 3,1). The classification of ICC defined the degree of consistency used to interpret ICC value: excellent (>0.9), good (0.76-0.9), moderate (0.5-0.75), and poor (lower than 0.50) [35].  
Bland-Altman analysis with 95% limits of agreement (LoA) was performed to access the agreements between datasets obtained during the two conditions. The scatterplot was constructed with partiality and the LoA is shown in the plot for the parameters registered. The mean score is represented on the x-axis, and the difference between two measurements (mean of the differences) is plotted on the y-axis. The 95% LoA is defined as \(\pm\)1.96 standard deviation (SD) (BA2). The average difference (Diff), the standard deviation of the differences (SD), and lower and upper 95% confidence level (CI) were from the formula: upper 95% CI = Diff \(-\) 1.96 \(\times\) SD; lower 95% CI = Diff + 1.96 \(\times\) SD [36]. The width of the boundaries of agreement and the distance of the mean through these differences were used to interpret measurement errors. An acceptable agreement between these two measurements was indicated when 95% of the data fell inside the LoA [37]. No significant difference between the measurements was reflected in case the line of equality was within the interval [38].
## 5 Conclusions
We also analyzed the influence of non-Gaussianity on the distribution of facet scattering and found that the introduction of a non-Gaussian large-scale sea surface constructed by the Tayfun model can make the scattering distribution deviate from the Gaussian distribution, but the deviation is very weak. In comparison, the distribution of the scattering with bispectrum correction deviates from the Gaussian distribution significantly, which is in good agreement with the Sentinel-1 data. Third, we analyzed the influence of swell on the scattering distribution simulated by the modified TSM. The results show that the introduction of swell widened the spread of the scattering distribution, and the peak value of the NRCS pdf with swell was smaller than that without swell. Additionally, the range of fluctuation of the NRCS profile with swell was larger than that without swell. All these changes caused by the introduction of swell made the distribution of the TSM-simulated facet scattering more consistent with the Sentinel-1 data. At the same time, the scattering image patterns and scattering image spectrum of Sentinel-1 data were successfully simulated at various sea states when the swell was considered. Similar results can be found in [46], where the textural features of the simulated SAR images with the consideration of swell were in good agreement with those of the RADARSAT-2 SAR images. Conceptualization, C.F. and Q.Y. ; methodology, Y.W. ; software, Y.W. ; validation, T.S., J.M. and J.Z. ; writing--original draft preparation, Y.W. ; writing--review and editing, C.F and Q.Y. ; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China under Grant 61931025, by the Key Program of Joint Fund of the National Natural Science Foundation of China and Shandong Province under Grant U2006207, and by the Fundamental Research Funds for the Central Universities under Grant 21 CX06015A. Not applicable. The authors would like to thank the European Space Agency for providing the Sentinel-1 data and the EUMETSAT for providing electronic access to the ASCAT data. The authors would also like to thank NDBC for providing the buoy data. The authors declare no conflict of interest.
## 5 Research on the Frequency-Domain Characteristics of AE Signals
### Wavelet Packet Energy Analysis of AE Signals
#### 5.2.2 Energy Distribution of Each Sub-Band  
When the AE signal is decomposed to the fourth layer, the analyzed signal is recorded as \(S_{4,j}\), and its corresponding energy is \(E_{4,j}\); then, the energy of each frequency band can be expressed as  
\[E_{4,j}=\int\left|S_{4,j}(t)\right|^{2}dt=\sum_{k=1}^{m}\left|x_{j,k}\right|^{2} \tag{5}\]  
where \(m\) is the number of discrete sampling points of the signal; \(x_{j,k}\) represents the amplitude of \(S_{4,j}\) discrete points of the signal.  
That is, if the total energy of the analyzed signal is \(E_{0}\), then  
\[E_{0}=\sum_{j=0}^{24-1}E_{4,j} \tag{6}\]  
The percentage of the energy of each sub-band of the signal in the total energy of the analyzed signal is calculated as follows:  
\[P_{j}=\frac{E_{4,j}}{E_{0}} \tag{7}\]  
Based on the above formula, the energy ratio distribution of the AE signal in each sub-band after wavelet packet decomposition can be obtained.
## 2 Mathematical Formulation  
### Governing Equations and Numerical Models  
Figure 3a,b presents the dimensions of the cylindrical computational domain for heat sinks with LED and heat sinks with LED and housing, respectively. The air jet impingement cooling in a confined area is generated using a cooling fan, which functions as a rotating machinery device. Version 5.3 of COMSOL Multiphysics software (COMSOL Inc., Burlington, MA, USA) is used for geometry modeling, grid meshing, physics modeling, solving, and post processing to simulate the results (COMSOL Inc. 2017) [1]. To accurately simulate heat sink heat dissipation performance under jet impingement cooling, the rotating machinery is coupled with nonisothermal flow interface with a standard \(k\)-\(\varepsilon\) turbulence flow model to examine the heat transfer in the turbulent flow field. Typically, the continuity equation governs mass conservation, and the Navier-Stokes equations govern the momentum balance. The rotating parts are kept frozen in position, and rotation is accounted for by the effects of the Coriolis and centrifugal forces. Thus, the numerical simulated rotating flow is fixed or frozen by assuming that the topology of the system relative to the rotating reference frame. This saves considerable computation time and resources for simulating the pseudo stationary formulation.  
The quasi-steady flow is computed with the interface of the rotating machinery. The axial, tangential, and radial flow velocity components can be simulated for numerical and experimental validation when the flow exits the round tube. Nonisothermal flow occurs in this CLRDL through temperature gradient (COMSOL Inc. 2017) [1]. The turbulent three-dimensional Navier-Stokes and energy equations negligible radiative heat transfer are numerically solved using computational fluid dynamics.  
The governing differential equations of continuity, momentum, and energy are as follows:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\mathbf{u})=0 \tag{1}\]  
\[\rho\frac{\partial\mathbf{u}}{\partial t}+\rho\mathbf{u}\cdot\
abla\mathbf{u}=-\
abla p+ \
abla\cdot\left[\mu\left(\
abla\mathbf{u}+  
The corresponding model constants for turbulence flow of the \(k\)-\(\varepsilon\) turbulence model are denoted by the following values: \(C_{\mu}=0.09\), \(C_{\varepsilon 1}=1.44\), \(C_{\varepsilon 2}=1.92\), \(\alpha_{\rm k}=1.0\), \(\sigma_{\varepsilon}=1.3\).  
Convective and conductive heat transfer is governed by the following equation:  
\[\rho C_{\rm p}\frac{\partial T}{\partial t}+\rho C_{\rm p}\mathbf{u}\cdot\
abla T =\
abla\cdot(kVT)+Q \tag{7}\]
## 3 Blockchain-Based DED Supervision Model and Architecture
### System Architecture  
This paper proposes a blockchain-based DED safety supervision system. Figure 2 shows the system architecture, which involves three parts: (1) physical layer; (2) smart contract module layer; (3) blockchain layer. The system has five types of users, i.e., the MAU, SAE, LE, BAE, and supervisor. The DED traceability system leverages blockchain to record the circulation information during the whole process. As in the system model in Figure 1, the participants in DED traceability need to verify the safety and the correctness of the received DED. They should also prevent DED safety accidents, such as loss accidents and fatal accidents because of premature blasts.  
Figure 1: DED supervision model.  
Figure 2: DED supervision system architecture.  
**Physical layer:** The physical layer collects DED-related data in a timely manner, which leverages the Internet of Things (IoT) including survey cameras, wireless sensors, and GPS trackers to monitor temperature, pressure, and other environmental conditions [8].  
**Information layer:** The information layer consists of five modules using the blockchain and smart contracts, the production transaction module, order transaction module, transportation transaction module, supervision module, and blasting module. The function of the production transactions module is to upload the work code to the NIEDCC, send the product information, and update the tag information.  
The production transaction module is mainly used to manage the MAU record and upload the work code to the NIEDCC. The order transaction module is mainly used to process DED order transactions between the SAE and the MAU. The transportation transaction module is used to monitor the environment of the DEDs' transport, which includes the data collection of the temperature, humidity, pressure, and so on. The supervision management module is used for monitoring the DEDs to prevent loss and circulation records, which involves uploading the circulation record to the supervision system to bind with the identification code and verifying the data. The blasting module is responsible for managing the blasting process and uploading the blasting record to the NIEDCC.  
**Blockchain layer:** The blockchain layer works as a peer-to-peer (P2P) network layer, which is used to integrate DED track-and-trace data for DED traceability. It records significant DED information regarding the circulation.
## 2 Impedance Spectroscopy
### Scopes of Application
#### 2.1.1 IS in Human Health Safety Due to Water Sources Contamination
The sensing material was assessed by EIS (a conventional cell composed of the working electrode, an Ag/AgCl-saturated KCl reference electrode, and a platinum slide counter electrode were used; the impedance plots were obtained by applying an AC voltage potential with an amplitude of 5 mV, possessing a DC component with 0.21 V, in the frequency range 0.1 Hz to 100 kHz) and showed very high sensitivity with a detection limit of around 0.027 pg/mL in the range of 0.005-1.0 ng/mL, as well as high selectivity for oxytetracycline in the presence of tetracycline, doxycycline, and chlorotetracycline (all antibiotics). Still considering the removal of water contaminants, the use of filters is also a common approach. Indeed, ultrafiltration, microfiltration, and nanofiltration can be carried out by membranes to provide the adequate removal of pathogenic bacteria, such as _Cryptosporidium_ and _Giardia_, among others. Filtering membranes used in the industry can have their performance and lifetime improved through EleIS evaluation. [PERSON] et al. [51] used the previously mentioned approach to check the life cycle of graphene industrial filters used for water remediation of acetonitrile (CH\({}_{3}\)CN) and 2,4-dichlorophenol (C\({}_{2}\)C\({}_{6}\)H\({}_{4}\)O) pollutants (they used these for the characterization of a four-wire measurement setup, in the frequency range from 1 to 100 kHz). So, in summary and as reported, the growing concerns regarding the presence of contaminants in food and water, even at relatively low concentrations, have contributed to the use of IS by researchers for detection/measuring purposes.
# WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
## 3 Results
### Water and Energy Fluxes  
The annual cycle of observed daily ET (Figure 5) showed different patterns in the biomes: (1) Cerrado and Pantanal with maximum water vapor flux in October, decreasing toward March and April as the wet season ends; (2) a well-defined ET cycle in the Amazonia site, peaking in September during the dry season. ET cycles in the Cerrado and Pantanal are similar, with maximum daily ET of approximately 7.0 mm day\({}^{-1}\). In July, daily ET values are similar in the Cerrado, Pantanal and Amazon sites, with a similar pattern to what was previously observed for solar radiation and air temperature (Figure 3A,B). The Caatinga site stood out, with values down to three-fold lower if compared to the other sites, despite the similar seasonal pattern (higher daily ET between October and April, lower daily ET between May and September). Maximum ET coincides with higher energy availability (radiation) and higher temperatures in the sites, between October and November.  
The monthly seasonal patterns of net radiation and sensible and latent heat fluxes differ strongly from site to site. In the Pantanal, Caatinga and Cerrado, net radiation (Rn) presents values higher than 120 W m\({}^{-2}\) (Figure 6A) in January and May, with a decreasing pattern the following months. The Amazon site, located further north than the other sites  
Figure 4: Monthly accumulated rainfall boxplots for each site and studied period (Amazonia: 2009–2011; Caatinga: January 2014–July 2015; Cerrado: 2004–2006; Pantanal: 2015–2016).  
Figure 5: Monthly daily evapotranspiration (mm day\({}^{-1}\)) boxplots for each site and studied period (Amazonia: 2009–2011; Caatinga: January 2014–July 2015; Cerrado: 2004–2006; Pantanal: 2015–2016).  
and therefore more influenced by the Intertropical Convergence Zone during these months, presented lower values, of approximately 100 W m\({}^{-2}\), with increasing values from May until the peak of the dry season in September. Hourly Rn patterns are similar, with the Amazon site featuring approximately 50 W m\({}^{-2}\) less than the other sites. However, latent heat flux (LE) (Figure 6B) and sensible heat flux (H) (Figure 6C) have distinct hourly and monthly patterns. Maximum LE in the Pantanal is three-fold higher than in the Caatinga, while the H pattern is the inverse. Rn, H and LE patterns are in phase regarding Pantanal and Amazonia sites, particularly because LE and H sharply increase in the dry season, following the increase in available energy. An opposite relationship is found between Rn and LE in the Cerrado and Caatinga sites, which increase in September and October while LE reduces in the same period.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
###### Abstract  
Current inventory-taking methods (counting stocks and checking correct placements) in large vertical warehouses are mostly manual, resulting in (i) large personnel costs, (ii) human errors and (iii) incidents due to working at large heights. To remedy this, the use of autonomous indoor drones has been proposed. However, these drones require accurate localization solutions that are easy to (temporarily) install at low costs in large warehouses. To this end, we designed a Ultra-Wideband (UWB) solution that uses infrastructure anchor nodes that do not require any wired backbone and can be battery powered. The resulting system has a theoretical update rate of up to 2892 Hz (assuming no hardware dependent delays). Moreover, the anchor nodes have an average current consumption of only 27 mA (compared to 130 mA of traditional UWB infrastructure nodes). Finally, the system has been experimentally validated and is available as open-source software.  
Ultra-Wideband (UWB); drone inventory; low-energy; easy installation; infrastructure-light +
Footnote †: journal: Sensors
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
###### Abstract  
With the popularity of smartphones and the development of microelectromechanical system (MEMS), the pedestrian dead reckoning (PDR) algorithm based on the built-in sensors of a smartphone has attracted much research. Heading estimation is the key to obtaining reliable position information. Hence, an adaptive Kalman filter (AKF) based on an autoregressive model (AR) is proposed to improve the accuracy of heading for pedestrian dead reckoning in a complex indoor environment. Our approach uses an autoregressive model to build a Kalman filter (KF), and the heading is calculated by the gyroscope, obtained by the magnetometer, and stored by previous estimates, then are fused to determine the measurement heading. An AKF based on the innovation sequence is used to adaptively adjust the state variance matrix to enhance the accuracy of the heading estimation. In order to suppress the drift of the gyroscope, the heading calculated by the AKF is used to correct the heading calculated by the outputs of the gyroscope if a quasi-static magnetic field is detected. Data were collected using two smartphones. These experiments showed that the average error of two-dimensional (2D) position estimation obtained by the proposed algorithm is reduced by 40.00% and 66.39%, and the root mean square (RMS) is improved by 43.87% and 66.79%, respectively, for these two smartphones.  
s 2019 9 3727 31 July 2019; Accepted: 3 September 2019; Published: 6 September 2019
## 3 Results and Discussion
### Chilling Injury Detection Based on Image Texture  
Different light scattering properties in normal and defective pulp tissue would bring about different patterns of spatial textures. The discriminant capability of the image-based features was evaluated by the three-class overall classification accuracy. Among the different combinations of scanning speeds and spectral resolutions, the overall classification accuracies varied from 64.2-70.0%, 52.5-62.5%, 59.2-67.5%, and 55.8-63.3%, respectively, for 8 mm/s and 20 mm/s using data with spectral resolution of 1.25 nm (Figure 8a) and 8 mm/s and 20 mm/s using data with spectral resolution of 5.03 nm (Figure 8b). The classification performance was not significantly improved with the increasing number of image textural features. The low classification results also could be observed in those of 2.51 nm and 10.08 nm (Figure S4). In comparison with spectral-based classification, the three-class  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{6}{c}{**Actual Class**} \\ \cline{2-7}  & \multicolumn{3}{c}{**1.25 nm, 8 mm/s *} & \multicolumn{3}{c}{**1.25 nm, 20 mm/s *} \\ \cline{2-7}  & **Normal** & **Slightly Chilling** & **Severely Chilling** & **Normal** & **Slightly Chilling** & **Severely Chilling** \\ \hline Normal & 41 & 2 & 0 & 40 & 2 & 0 \\ Slightly chilling & 3 & 44 & 6 & 3 & 45 & 5 \\ Severely chilling & 0 & 5 & 19 & 0 & 5 & 20 \\ Accuracy (\%) & 93.2 & 86.3 & 76.0 & 93.0 & 86.5 & 80.0 \\ \hline \multirow{2}{*}{**Prediction**} & \multicolumn{3}{c}{**5.03 nm, 8 mm/s *} & \multicolumn{3}{c}{**5.03 nm, 20 mm/s *} \\ \cline{2-7}  & **Normal** & **Slightly Chilling** & **Severely Chilling** & **Normal** & **Slightly Chilling** & **Severely Chilling** \\ \hline Normal & 43 & 1 & 0 & 44 & 1 & 0 \\ Slightly chilling & 0 & 49 & 2 & 0 & 50 & 3 \\ Severely chilling & 0 & 3 & 22 & 0 & 4 & 18 \\ Accuracy (\%) & 100 & 92.5 & 91.7 & 100 & 90.1 & 85.7 \\ \hline \hline \end{tabular} Note: the optimal number of spectral features for a, b, c and d were 5, 6, 6 and 6, respectively.  
\end{table}
Table 2: Confusion matrixes of different combinations of scanning speeds and spectral resolutions based on the optimal number of spectral features.  
Figure 7: There-class performance comparison based on spectral features at different scanning speeds and spectral resolutions of 1.25 nm (**a**) and 5.03 nm (**b**), respectively.  
performance using image-based features was much poor, indicating that image-based features were not feasible for detecting chilling damage in green jujubes. The high misclassification error had been attributed to the fact that no obvious symptom could be found in slightly chilling samples, resulting in the misclassification between normal and slightly chilling classes.