esa-sceva/satcom-chunk-collection huggingface dataset:
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 5 Pattern Matching-Based Load Anomaly Detector  
The anomaly detector proposed in this study is designed to ensure the security of the stand-alone system. To both overcome the shortcomings of general regression model-based detection methods and the imprecision of the traditional supervised learning method, the detector is proposed with a hybrid combination construction [5]. Meanwhile, to further improve the detection accuracy, the detection threshold is designed to be automatically adjusted over time. The pattern matching model used in this study is an improved SVM composed of an SVM and the FOA.
## 1 Introduction
### Smart Helmet and Display Interface  
The smart helmet combines a one-eyed transparent display designed to visualize augmented reality information on top of the firefighter's view. Figure 0(a) depicts the prototype generated within the European project, where the smart helmet is connected to several sensors such as a thermal camera and a GoPro attached to the helmet structure. Its main objective is to provide additional information to the firefighter during missions without compromising the integrity of their original equipment or obstructing their sight.  
The display primarily functions as a screen with the peculiarity that black pixels are transparent to the viewer. The information is rendered inside the display using the AR Interface. This interface was programmed using Angular, as detailed in [10], and has been designed modularly. This allows information from different sources to be displayed or removed independently, considering the data required for each situation based on feedback from the FRs.  
The need to adapt the quantity of information displayed in the interface during a realistic rescue scenario has been highlighted by the FRs [9; 10]. In this context, approximate measurements of cognitive load to regulate the quantity of information have been analyzed [20]. However, which awareness modules should be prioritized or eliminated  
Figure 1: Smart helmet and awareness-enhancing module symbols display interface designed during the RESCUER European H2020 project.  
according to the requirements of the end-users was not explored. The main goal of this study has been to establish a standard modularity of awareness modules according to the distinct information needs defined by the different FRs, depending on their roles and rescue task configurations.
## 2 Experimental Setup  
Experiments were performed to monitor the cure shrinkage of epoxy. A simple brick-shaped epoxy block was chosen for the study. Liquid epoxy was poured into a metal mold to a height of 6 cm. The mold was 10 cm high and had a rectangular bottom of 40 \(\times\) 10 cm. The setup is shown in Figure 1. The epoxy was a mixture of ten parts Epikote Resin MGS RIMR 135 and three parts Epikure Curing Agent MGS RIMH 137 [39]. The two components reacted with each other and turned into a solid block after about five hours. More details about the reaction are described in [39].  
The single-mode optical fibre from OFS Fitel, LLC with an operating wavelength of 1550 nm, core, clad and coating diameters of respectively 6500 nm, 0.125 mm and 0.155 mm, Pyrocoat(r) coating and an operating temperature between \(-65\) to +300 degrees Celsius (visualized by a black thread in Figure 1) was put into the mould before pouring the epoxy. The fibre was held in a horizontal position in the middle of the epoxy (about 3 cm high) by leading it through two small holes in the mold. Rubber, applied from the inside, was used to hold the fibre in place and prevent the epoxy from seeing through the holes. An \"OBR 4600\" from Luna Instruments [40] was used to detect the OBR signal with the following settings: (virtual) gauge length = 1.0 cm, sensor spacing = 1 mm, strain resolution \(<\) 30 ppm [41].  
Measurements were taken with the OBR in every 15 min for temperatures below and every 3 min above 40 degrees Celsius. A single measurement took approximately 5 s. The temperature was determined with Radial Glass NTC Thermistors, which were embedded into the epoxy. These were encapsulated in glass, have 10 k\(\Omega\) resistance at 25 degrees Celsius and have a temperature range from \(-40\)\(\rightarrow\)\(250\) degrees Celsius. See [17] for details of the temperature measurements setup. Initially, the optical fiber is surrounded by liquid epoxy. Strains do not exist in the liquid, and no strains are transferred to the fiber. This state is referred to a zero strain, and it is the same strain along the length of  
Figure 1: Upper image: schematical image of the experimental setup. Lower image: photo of the steel mold built to hold the epoxy during the experiment. The black thread visualizes the position of the optical fiber. The thread was replaced by the optical fiber before epoxy was cast into the container.  
the fiber. However, the fiber will experience the increased temperature due to the exothermic reaction heat of the epoxy. The temperature increase leads to an apparent positive strain along the length of the fiber. It is important that this signal due to elevated temperatures is not interpreted as actual strain onto the fiber.  
The epoxy will obtain an increase in viscosity as more chemical bonds form. Gradually, the epoxy will become a solid material. At some point in the curing process, the epoxy will adhere to the optical fiber and be able to transfer forces and strain into the fiber. More details about the experimental setup and the stages of the curing process are presented in [17].
## 2 Materials and Methods
### Seismic Data Description  
Data from the Neapolitan volcanoes were recorded by the seismic station network managed by the \"Istituto Nazionale di Geofisica e Vulcanologia, sez. Napoli-Osservatorio Vesuviano (INGV-OV)\". The digital seismic stations installed at Campi Flegrei (ASB2, AMS2, TAGG, and BGNG) and Vesuvius (BKSG, BKWG, and FAL2) are equipped with broadband three-component seismometers Lennartz LE-3D/20 s or Guralp CMG40T/60 s [15; 50]. At Iscchia, the digital stations T1361, T1362 T1363, T1364, and T1365 are equipped with short-period 1 Hz sensors Lennartz LE-3 Dlite, while IOCA holds a broadband seismometer Guralp CMG40T/60 s [9]. Depending on the station, data are sampled at a frequency of 100 or 125 Hz. A map with the location of the stations and the investigated area (Neapolitan volcanoes) is reported in Figure 1.  
For the Campi Flegrei caldera, we selected data recorded in 2006, when a relevant low-frequency hydrothermal component in the background seismic signal was evidenced by [36] and interpreted as related to fluid injections from a deeper geothermal reservoir  
Figure 1: Map of the Neapolitan volcanoes (Vesuvius, Campi Flegrei, and Ischia) with the seismic stations (circles in blue for Vesuvius, red for Campi Flegrei, and yellow for Ischia) used for the analysis.  
toward the shallower hydrothermal system of the Solfatara. On Ischia, we considered samples recorded after the Mw 3.9 earthquake, which occurred on 21 August 2017, when the hydrothermal source was particularly active and its persistent signature was identified in background noise [9; 25]. At Vesuvius, the seismic noise was (randomly) sampled during the first months of 2019. At Colima volcano, data were collected in 2006, during a temporary seismic survey organized by the Istituto Nazionale di Geofisica e Vulcanologia (INGV)-Osservatorio Vesuviano, the Observatorio Vulcanologico de Colima of Colima University (OVC) and the Instituto Andaluz de Geofisica (IAG), University of Granada, Spain [51]. We used seismograms recorded by three digital seismic stations (COME, COCA, and COBA) consisting of broadband three-component seismometers Lennartz LE-3D/20 s; the sampling rate was at 62.5 Hz. A map with the location of the stations and the investigated area (Colima volcano) is reported in Figure 2.
# GNSS Multipath Detection Using Continuous Time-Series C/N0
## 6 Conclusions  
This study demonstrated the novel utilization of the C/N\({}_{0}\) of the GNSS to detect NLOS signals efficiently. Although C/N\({}_{0}\) values have been used to select GNSS observations with improved quality for many years, most of the previous concepts were based on methods whereby satellites with signal strength over the C/N\({}_{0}\) threshold were not removed from positioning. In reality, the elevation-dependent threshold of the C/N\({}_{0}\) setting is somehow effective in mitigating multipath errors. However, the fluctuations in C/N\({}_{0}\) affected by NLOS signals are quite large. If the C/N\({}_{0}\) is above the threshold, the satellite is used for positioning even if it is still affected by the NLOS signal, which causes the positioning error to jump easily over several tens of meters. To overcome this issue, we focused on the values of the continuous time-series C/N\({}_{0}\) for certain periods. If the C/N\({}_{0}\) of the satellite is less than the determined threshold, the satellite is not used for positioning for a certain period, even if the C/N\({}_{0}\) recovers over the determined threshold. This new concept is very simple to implement and does not require other equipment or sensors. There are two key parameters for this new method: the determined threshold and the period during which the satellite is not used for  
Figure 24: Comparison of DGNSS RMS errors in different periods.  
Figure 25: Comparison of DGNSS RMS errors in different thresholds.  
positioning. We determined the suitable values for the two parameters based on the test data, which were subsequently evaluated.  
Three static tests were conducted at three different challenging locations near high-rise or medium-rise buildings in Tokyo. The test results demonstrated that our new approach could mitigate multipath errors from over several tens of meters to several meters in the DGNSS by detecting NLOS signals. Therefore, the performance of the RTK-GNSS was also improved. Moreover, the standard residual check in the least-squares method was found to be effective in reducing large pseudo-range errors with our new method.  
N.K. conceived and designed the experiments; N.K. performed the experiments and analyzed the data; R.F. and K.K. also partly analyzed the data. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the JSPS KAKENHI, grant number JP16K0532.  
The authors would like to thank the anonymous reviewers and editors for their help in improving the paper.  
The authors declare no conflict of interest.
## 3 Prototyping of the Proposed CAPT Framework
### Original Implementation
```
0: utterance sample \(\mathbf{x}\), coefficients \(\mathbf{W1,W2}\), \(B\) and \(\mathbf{ETA}\)
0: decision RETURN
1: Initialization
2:\(\mathbf{H1}\leftarrow\mathbf{x}\times\mathbf{W1}\)
3:\(\mathbf{\delta1}\leftarrow(\mathbf{H1}>0).\times\mathbf{H1}\)
4:\(\mathbf{\delta2}-\mathbf{\delta1}\times\mathbf{W2}+B\)
5:\(\delta2\gets 1/(1+\exp(-\hbar 2))\)
6:RETURN\(\leftarrow\delta2\geq\mathbf{ETA}\)
```  
**Algorithm 2** Pseudocode of the original CAPT implementation \begin{table}
\begin{tabular}{l l l l} \hline \hline
**RTL Ports** & **I/O** & **Bits** & **Protocol** \\ \hline \(CLK\) & I & 1 & ap\_ctrl\_hs \\ \(RST\) & I & 1 & ap\_ctrl\_hs \\ \(START\) & I & 1 & ap\_ctrl\_hs \\ \(DONE\) & O & 1 & ap\_ctrl\_hs \\ \(IDLE\) & O & 1 & ap\_ctrl\_hs \\ \(READY\) & O & 1 & ap\_ctrl\_hs \\ \(RETURN\) & O & 1 & ap\_ctrl\_hs \\ \hline \(x\_ADDRESS\) & O & \(2^{\#}\) (\(\overline{n}\in\mathbb{N}^{+}\)) & ap\_memory \\ \(x\_CE\) & O & 1 & ap\_memory \\ \(x\_Q\) & I & 32 & ap\_memory \\ \hline \(WI\_ADDRESS\) & O & \(2^{\#+6}\) (\(\overline{n}\in\mathbb{N}^{+}\)) & ap\_memory \\ \(WI\_CE\) & O & 1 & ap\_memory \\ \(WI\_Q\) & I & 32 & ap\_memory \\ \hline \(W2\_ADDRESS\) & O & \(2^{6}\) & ap\_memory \\ \(W2\_CE\) & O & 1 & ap\_memory \\ \(W2\_Q\) & I & 32 & ap\_memory \\ \hline \(B\) & I & 32 & ap\_none \\ \(ETA\) & I & 32 & ap\_none \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Interface protocol of the original CAPT IP core. Figure 3: Diagram of the finite state machine of the original C++ implementation: \(S*\) is the state of identification, \(L*\) is the line number of Algorithm 3, \(n\) and \(m=m_{1}+m_{2}\) are the iteration numbers. ```
0: utterance sample \(\textbf{x}[m]\), coefficients \(\textbf{W1}[n\times m]\), \(\textbf{W2}[n]\), \(B\) and \(ETA\)
0: decision RETURN
1: Initialization
2:for\(i\in\{1,2,\dots,m\}\)do
3:\(reg\gets 0\)
4:for\(j\in\{1,2,\dots,n\}\)do
5:\(reg\gets reg+\textbf{x}(j)\times\textbf{W1}(i,j)\)
6:endfor
7:\(\textbf{M}(i,j)=reg\)
8:endfor
9:for\(i\in\{1,2,\dots,m\}\)do
10:if\(\textbf{M}(i)\leq 0\)then
11:\(\boldsymbol{\delta}\textbf{1}(i)\gets 0\)
12:else
13:\(\boldsymbol{\delta}\textbf{1}(i)\leftarrow\textbf{M}(i)\)
14:endif
15:endfor
16:\(reg\gets 0\)
17:for\(i\in\{1,2,\dots,m\}\)do
18:\(reg\gets reg+\boldsymbol{\delta}\textbf{1}(i)\times\textbf{W2}(i)\)
19:endfor
20:\(h2\gets reg+B\)
21:\(\delta 2\gets 1/(1+\exp(-h2))\)
22:if\(\delta 2\geq ETA\)then
23:\(RETURN\gets true\)
24:else
25:\(RETURN\leftarrow false\)
26:endif
```  
**Algorithm 3** Pseudocode of the C++ CAPT implementation
# Design of 1-Bit Digital Reconfigurable Reflective Metasurface for Beam-Scanning
## 3 Results and Discussion  
### Simulation Results of the 1-Bit Digital RRM Element  
When the working frequency changes, the 180\({}^{\circ}\) phase compensation can be acquired by controlling the capacitance in broadband as given in Figure 5. The varactor diode can alter the phase compensations of all the RRM elements by controlling the bias voltage across the varactor diode. By tuning the capacitance value, the phase compensation curves are plotted in Figure 5a. It can be clearly observed that when the capacitance varies, the range of the phase compensation of the 1-bit digital element is more than 180\({}^{\circ}\). We can see that the element can work between 3 GHz and 4 GHz with over 180\({}^{\circ}\) phase compensation. With this characteristic, the RRM can work in a wide bandwidth of 3 GHz to 4 GHz. Therefore, the varactor diode has more potential applications than the PIN diode in the RRM design.  
Finally, we select 3.5 GHz as the operating frequency to design the RRM. Based on the simulation result in Figure 5b, the phase compensation is \(-145^{\circ}\) when \(Cap=1.1\,\mathrm{pF}\) and the phase compensation is \(35^{\circ}\) when \(Cap=1.9\,\mathrm{pF}\). We select \(-145^{\circ}\) as the coding \"0\" and \(35^{\circ}\) as the coding \"1\". Of course, we can also choose an operating frequency between 3 GHz and 4 GHz to design the 1-bit element by changing the capacitance value.  
Figure 4: Reflection phase distribution on the RRM when \(\theta_{r}=60^{\circ}\) (**a**) Accurate phase distribution; (**b**) Digital phase distribution.  
Figure 3: Reflection phase distribution on the RRM when \(\theta_{r}=30^{\circ}\) (**a**) Accurate phase distribution; (**b**) Digital phase distribution.  
Depending on the fuzzy quantification theory, these elements are divided into two groups, corresponding to \(35^{\circ}\) and \(-145^{\circ}\). If the phase compensation of the unit is in the range \(125\)-\(305^{\circ}\), the element is coding \"1\", and the rest is coding \"0\". The concept of a digital system means that we transform the phase compensation of a cell into coding \"0\" and coding \"1\" of 1-bit. By such a transformation, the RRM generates a digital concept. The digital RRM can realize the beam-scanning performance by changing the coding arrangement. In order to reduce the difficulty of design and processing, we choose the fixed capacitors of \(Cap=1.1\) pF and \(Cap=1.9\) pF to replace the varactor diodes. Because the phase compensations of elements are not calculated by the formulas of reflectarray, this kind of digital phase compensations are not the precise phase compensations. As a result, the gain of the RRM will be less than the current state of the art. In contrast, the digital RRM improves the bandwidth performance.
## 1 Introduction
### Aims and Specifics of the Current Research  
The originality of the current study lies in the development of a thermo-electric model for the assessment of temperature distribution in PV module. In this paper both, thermal and electric models of the PV module are described with dynamic modeling. The dynamic thermal model is used from [10]. While the dynamic electric model is presented by a double diode PV cell model, including electrical parameters as a function of solar radiation and the operating temperature of the PV module. Modeling of the thermo-electric model of the PV module was performed in Matlab/Simulink software package using the s-function block diagram. Validation of the presented thermo-electric model is based on meteorological and experimental data of the PV system used in this study. By accurately determining the temperature distribution in the PV module, also lifetime of the PV module can be predicted. In addition, two other models for determining the temperature distribution in the PV module and the output power are included. The temperature distribution in the PV module was calculated using the Ansys Transient Thermal software package, and the already confirmed empirical equation [10; 24; 25; 26; 27; 28; 29] was used to calculate the output power of the PV module. In addition to validating the presented dynamic thermo-electric model, the importance of accuracy of measuring devices and optical losses is also included. Various authors [10; 12; 13; 23; 30] used constant values of optical losses in their models to determine the temperature distribution of fixed PV modules. Even though, in this paper measurements are performed on a dual-axis tracking system, the optical losses were calculated as a function of an incident angle of the Sun's rays, which is similar as in the following studies [31; 32]).  
The paper consists of five sections. The first section provides an introduction and literature review. The second section presents the methodology and measurement site, the third section includes some results of the proposed model and the fourth section presents theidiscussion of the results compared to other studies. The last section presents the essential findings or conclusions.
## 2 Trajectory Design and Optimization
### Trajectory Optimization: Case of Constrained Clock Angle  
Consider now the case in which the clock angle can take only a finite number of values in the admissible interval \([0,\,360]^{\circ}\). Assume, for example, that the angle \(\delta\) can take four values only, that is, \(\delta\in\mathcal{I}\), where \(\mathcal{I}\) is a discrete set. In this case, at a given point of the transfer trajectory, the function \(\mathcal{H}_{c}\) can take only four values depending on the selected sail clock angle. Therefore, the optimal clock angle can be easily obtained using a (numerical) vector sorting procedure [28] that finds the value of \(\delta\) that maximizes \(\mathcal{H}_{c}\).
## 3 Simulation Analysis and Results
### Simulation Setup  
Taking the ellipse-compensated layout as an example, the system model is shown in Figure 4. Assuming that the size of the spatial area to be covered is 10 m \(\times\) 10 m \(\times\)\(d\). The LED light sources are located on the \(X\)-0-\(Y\) plane, and the initial position of the LED light source is (\(X_{i}\), \(Y_{i}\), 0). The receiving plane is parallel to the transmitting plane with a depth of \(d\) m.  
The number of LED light sources used in the three light source layouts is 16. The positions of LED light source in the ellipse-compensated layout are (2.2, 0, 0), (\(-\)2.2, 0, 0), (1.83, 1.06, 0), (1.83, \(-\)1.06, 0), (\(-\)1.83, 1.06, 0), (\(-\)1.83, \(-\)1.06, 0), (0, 1.9, 0), (0, \(-\)1.9, 0), (0.98, 1.70, 0), (0.98, \(-\)1.70, 0), (\(-\)0.98, 1.70, 0), and (\(-\)0.98, \(-\)1.70, 0). The positions of LED light sources in the ring-compensated layout are (0, 1, 0), (0, \(-\)1, 0), (1, 0, 0), (\(-\)1, 0, 0), (0, 2, 0), (0, \(-\)2, 0), (2, 0, 0), (1.41, 1.41, 0), (1.41, -1.41, 0), and (\(-\)1.41, -1.41, 0), respectively. The LED positions in the lemin  
Figure 4: Schematic diagram of ellipse-compensated layout.  
Figure 3: (**a**) Ellipse-compensated layout; (**b**) ring-compensated layout; (**c**) lemniscate-compensated layout.  
(\(-2.2\), 0, 0), (1.87, 1.1, 0), (1.87, \(-1.1\), 0), (\(-1.87\), \(1.1\), 0), (\(-1.87\), \(-1.1\), 0), (1.2, 1.9, 0), (1.2, \(-1.9\), 0), (\(-1.2\), \(1.9\), 0), (\(-1.2\), \(1.9\), 0), (\(-1.2\), \(-1.9\), 0), (\(0\), \(-1.1\), 0), and (0, 1.1, 0), respectively. The positions of the four compensated light sources are (2.2, 1.9, 0), (2.2, \(-1.9\), 0), (\(-2.2\), 1.9, 0), and (\(-2.2\), \(-1.9\), 0). The detail simulation parameters used in the UOWC system model are shown in Table 2.
## 3 Results and Discussion
### UHPLC-HRMS-MS Characterization
Procyanidin B1, naringenin, chrysin, vanillic acid, p-cumaric acid, caffeic acid and quinic acid, present in the tested BC pomace, were also identified in BC cultivars by other authors [36]. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Compound**} & \begin{tabular}{c} **Chemical** \\ **Formula** \\ \end{tabular} & \begin{tabular}{c} **Monitored Ion** \\ **[M-H]**– \\ \end{tabular} & \
\begin{tabular}{c} **Retention Time** \\ **(min)** \\ \end{tabular} &
\begin{tabular}{c} **Amount (mg/100** \\ **g d.w.)** \\ \end{tabular} \\ \hline Chlorogenic acid & C\({}_{16}\)H\({}_{18}\)O\({}_{9}\) & 353.08780 & 6.17 & 9045.88 \\ Caffeic acid & C\({}_{9}\)H\({}_{8}\)O\({}_{4}\) & 179.03501 & 6.6 & 0.265 \\ Epicatechin & C\({}_{15}\)H\({}_{14}\)O\({}_{6}\) & 289.07180 & 5.48 & 791.44 \\ Catechin & C\({}_{15}\)H\({}_{14}\)O\({}_{6}\) & 289.07180 & 3.29 & 65.55 \\ Syringic acid & C\({}_{9}\)H\({}_{10}\)O\({}_{5}\) & 197.04560 & 4.13 & 11.41 \\ p-Coumaric acid & C\({}_{9}\)H\({}_{8}\)O\({}_{3}\) & 163.03950 & 6.76 & 45.10 \\ Fertile acid & C\({}_{10}\)H\({}_{10}\)O\({}_{4}\) & 193.05070 & 12.63 & 27.60 \\ Hyperoside (quercetin-3-O-galactoside) & C\({}_{21}\)H\({}_{20}\)O\({}_{12}\) & 463.08768 & 10.82 & 757.85 \\ Rutin & C\({}_{27}\)H\({}_{30}\)O\({}_{16}\) & 609.14610 & 12.6 & 1098.05 \\ Naringin & C\({}_{27}\)H\({}_{32}\)O\({}_{14}\) & 579.17185 & 9.96 & 11.96 \\ Abscisic acid & C\({}_{15}\)H\({}_{20}\)O\({}_{4}\) & 263.12890 & 14.82 & 13.49 \\ Gallic acid & C\({}_{7}\)H\({}_{6}\)O\({}_{5}\) & 169.01430 & 11.41 & 27.62 \\ Quercetin & C\({}_{15}\)H\({}_{10}\)O\({}_{7}\) & 301.03540 & 13.59 & 564.80 \\ Chrysin & C\({}_{15}\)H\({}_{10}\)O\({}_{4}\) & 253.05066 & 14.69 & 22.61 \\ Pinocembrin & C\({}_{15}\)H\({}_{12}\)O\({}_{4}\) & 255.06630 & 18.45 & 25.19 \\ Naringenin & C\({}_{15}\)H\({}_{12}\)O\({}_{5}\) & 271.06120 & 15.64 & 5.93 \\ Daidzin & C\({}_{21}\)H\({}_{20}\)O\({}_{9}\) & 415.10348 & 16.26 & 0.49 \\ Daidzein & C\({}_{15}\)H\({}_{10}\)O\({}_{4}\) & 253.05066 & 19.08 & 0.23 \\ Vanillic acid & C\({}_{8}\)H\({}_{8}\)O\({}_{4}\) & 167.03500 & 8.68 & 47.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative analysis by UHPLC-HRMS, with the structures of the BC pomace compounds confirmed by comparison with reference standards.