esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 4 Building a QKD Emulation System to Test Protocols
### Data Transmission  
Next, the user can manually press a button to pulse the laser or hold the button down for a couple of seconds to generate a continuous beam. The laser beam is polarized through [PERSON]'s half-wave plate, then travels to [PERSON]'s half-wave plate. [PERSON]'s half-wave plate represents \"measurement\" of the beam. At this stage, if [PERSON] and [PERSON] used \"matching bases\", the laser beam should be either purely vertically or horizontally polarized. To illustrate this, the eight possible combinations for [PERSON] and [PERSON]'s basis choices are shown in Figure 4. Scenarios 1 through 4 represent [PERSON] measuring in the rectilinear bases, and scenarios 5 through 8 represent [PERSON] measuring in the diagonal basis. Notice that when they use mismatching bases (scenarios 3, 4, 5, 6), the resulting polarization is diagonal, and contains both horizontal and vertical components. This is important because at the polarizing beamsplitter cube, the horizontally polarized light is transmitted, while the vertically polarized light is reflected. If [PERSON] and [PERSON] use matching bases and have only vertically or horizontally polarized light, then only one of the photodetectors should receive the full intensity of the beam, and one of the LEDs on the photodetectors should light up. If they use mismatching bases, and the beam is diagonally polarized, then both photodetectors should receive part of the beam's intensity. The kit includes additional electronics that light up the LED on one of the detectors randomly to simulate a random result due to mismatching bases. The user must manually interpret the results of the LED indicator. The remaining steps are identical to BB84. To incorporate [PERSON], [PERSON]'s hardware can be placed between [PERSON] and [PERSON].  
Figure 4: The eight possible basis combinations for [PERSON] and [PERSON]’s half-wave plates. The dotted gray line represents 0 rotation of the half-wave plate. The pink axis represents the alignment of [PERSON]’s half-wave plate. The pink axis will either match the dotted gray line when in the rectilinear basis, or will be rotated 22.5\({}^{\circ}\) when in the diagonal basis. The black arrows in each scenario labeled “in” represent the polarization of the incident light before passing through the plate. The blue arrows labeled “out” represent the resulting polarization after passing through the plate. Rotating [PERSON]’s half-wave plate represents the “measurement” stage. In scenario 1, the input is horizontally polarized and [PERSON] is using the rectilinear basis, so the resulting light is still horizontally polarized. In scenario 5, the input is horizontally polarized, but [PERSON] measures in the diagonal basis, so the resulting light is diagonally polarized at 135\({}^{\circ}\).
# UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station
## Abbreviations  
\begin{tabular}{l l l} \(q_{s}\) & amount of water supplied to the system & kg \\ \(q_{mix}\) & flow rate of water vapor in the mixed flow of hydrogen and water & kg \\ \(q_{\rm H_{2}}\) & hydrogen production & kg \\ \(N\) & input power of the system and are the heat carried by oxygen & J \\  & and hydrogen & J \\ \(Q_{\rm O_{2}},Q_{\rm H_{2}}\) & heat carried by oxygen and hydrogen & J \\ \(Q_{E}\) & electrolysis cell & J \\  & heat dissipated to the environment by the oxygen & \\ \(Q_{\rm O_{2}}^{xx},Q_{\rm H_{2}}^{xx},Q_{\rm H_{2}}^{xx}\) & heat exchanger, the hydrogen heat exchanger, and the & J \\  & water-gas separator, respectively & \\ \(Q^{mix}_{H_{2}}\) & heat carried by water vapor in the mixed flow of & J \\  & hydrogen and water & \\ \(Q_{\rm H_{0}}\cdot q_{\rm H_{0}O}\) & theoretical power consumption of electrolyzed water & J \\ \(Q_{cold}\) & heat taken away by the cold mass through the heat exchanger & J \\ \(Q_{H_{\rm{i}O}}\) & theoretical power consumption required for water electrolysis & J \\ \(P_{H_{\rm{i}O}}\) & theoretical power consumption per unit mass of water electrolysis & J/kg \\ \(q_{\rm H_{2}O}\) & mass of water being electrolyzed & kg \\ \(q_{vac}\) & mass of water entering the electrolytic core & kg \\ \(q_{nt}\) & mass of water discharged from the cathode & kg \\ \(Q_{\rm WC}\) & heat taken away from the outlet of the electrolytic cell & J \\  & heat taken away by the water discharged from the & J \\ \(Q_{C}\) & electrolytic core & J \\  & heat dissipation coefficient of the electrolytic cell to & \\ \(K_{c}\) & the environment & J/(m\(\cdot^{\circ}\)C) \\ \(F_{C}\) & total heat dissipation length of the electrolyzer & m \\ \(T_{C}\) & temperature of the electrolytic cell & \({}^{\circ}\)C \\ \(T_{A}\) & ambient temperature & \({}^{\circ}\)C \\ \(Q_{\rm O_{2}C}\) & heat removed by oxygen & J \\ \(Q_{H_{2}C}\) & heat removed by hydrogen respectively & J \\  & mass flow rate of water vapor carried by the cathode and & kg/s \\  & anode respectively & \\ \(T_{\rm Cl},T_{\rm CO}\) & inlet and outlet temperature of the electrolytic core respectively & \({}^{\circ}\)C \\ \(Q_{\rm O_{2}w},Q_{H_{2}w}\) & heat taken away by anode water vapor and cathode water vapor & J \\ \(c_{p},c_{p}c_{p}c_{p},c_{p}H_{2}\) & specific heat capacities of water, hydrogen, and oxygen & J/(kg\(\cdot^{\circ}\)C) \\ \(\lambda\) & latent heat of vaporization of water & J \\ \(P_{\rm chain}^{chain}\) & oxygen partial pressure & kPa \\ \(P_{t}^{chain}\) & total pressure & kPa \\ \(M_{air}\) & molar mass of air & kg/s \\ \(R_{a}\) & Ideal gas constant & \\ \(T_{c}\) & temperature in cabin & \({}^{\circ}\)C \\ \(V_{c}\) & total volume in cabin & m\({}^{3}\) \\ \(w_{\rm O_{2}}^{chain}\) & mass flow of oxygen into the cabin & kg/s \\ \(w_{\rm N_{2}}^{chain}\) & the mass flow of nitrogen into the cabin & kg/s \\ \(w_{\rm CO_{2}}^{chain}\) & mass flow of carbon dioxide into the cabin & kg/s \\ \end{tabular}
## 2 Materials and Methods
### Agricultural Plastic Surfaces (APS) Analysis
#### 2.2.2 APS Mapping by Orthophoto Visual Interpretation  
The result of the previous operation was vectorized through QGIS and used as a starting point for the complete digitization of the APS remains. The 2017 orthophotos [27] were used, and these have a 50 cm resolution that allows an accurate and true distinction of the plastics in the same period of the satellite analysis (summer 2017).  
True-color orthophotos are another type of digital imagery that are commonly employed. These can be processed through semi-automated procedures to facilitate APS detection [36]. In this case study, given the extent of the study area and the density of APSs, manual digitization was adopted to complement the semi-automatic remote-sensed process previously presented. Specifically, false-detection errors derived from the use of the RPGI index were eliminated and non-detected APSs were manually digitized through visual interpretation. This was possible due to the ease of distinguishing greenhouse plastics from other landscape components thanks to particular color and shape and due to the very high resolution of the orthophotos (50 cm). The polygonization of the APS enabled performing of complex spatial analyses (Figure 4), which are fundamental for the overall planning of the rural territory [37]. The final result is a vectorial output in which the total consistency and location of the APS in terms of hectares at July 2017 is reported.  
Figure 4: Example extracts of the APS mapping result for two different portions of the study area.
# Place Recognition: An Overview of Vision Perspective
## 2 Traditional Image Descriptors
### Global Image Descriptors  
The key difference between local and global place descriptors is the presence of the detection phase. One can easily figure out that local place descriptors turns into global descriptors by predefining the key points as the whole image. WI-SURF [30] used whole-image descriptors based on SURF features, and BRIEF-GIST [31] used BRIEF [26] features in a similar whole-image fashion.  
A representative global descriptor is GIST [5]. It has been shown to suitably model semantically meaningful and visually similar scenes in a very compact vector. The amount of perceptual and semantic information that observers comprehend at a glance (around 200 ms) refers to the gist of the scene, termed Spatial Envelope properties, and it encodes the dominant spatial-layout information of the image. GIST uses Gabor filters at different orientations and different frequencies to extract information from the image. The results are averaged to generate a compact vector that represents the \"gist\" of a scene. Reference [32] applied GIST to large-scale omnidirectional imagery and obtained a good segmentation of the search space into clusters, e.g., tall buildings, streets, open areas, mountains. Reference [33] followed a biological strategy that first computes the gist of a scene to produce a coarse localization hypothesis, then refine it by locating salient landmark points in the scene.  
The performance of techniques described in Section 2 mainly depends on the size of codebook \(\mathbf{C}_{j}\) if too small, the codebook does not characterize the dataset well, while if it is too large, it requires huge computational resources and time. While global image descriptors have their own disadvantages, they usually assume that images are taken from the same viewpoint.
## 3 Performance Analysis
### Tests of the Fitted Model  
To evaluate the adjusted model, samples were taken from the wireless repeater located approximately 80 m from the hotspot, as shown in Figure 10. A total of 560 real signal samples were collected using a Raspberry Pi 3 and a GPS module, as shown in Figure 10. Subsequently, using the data and Matlab tools, a heat map was generated based on the real samples, which is presented in Figure 11. This result was compared with the heat map obtained from the adjusted model, shown in Figure 9; it is apparent that the heat maps do not fully correspond, underscoring the need for further refinement of the model to achieve a closer alignment with the empirical observations.  
Figure 10: Location map of the 560 empirical samples obtained the received power levels by Raspberry Pi 3 and GPS from the repeater the seal WiFi.  
Figure 9: Received power levels vs distance using Equation (8) the fitted model, at the site where the WiFi signal repeater is installed.  
The WiFi repeater, located in the central park of the Checa parish (Figure 11), displays the different signal strengths through a color palette: green represents the strongest signal at \(-43\) dBm while red indicates the weakest signal at \(-97\) dBm.
# Thermal Effects on Photovoltaic Array Performance: Experimentation, Modeling, and Simulation
## 2 Experimental Setup  
This work includes data gathered from a solar array located on the roof of the CanmetENERGY facility, located in Varennes, QC, Canada as shown in Figure 1. The PV array began operation on 21 April 2018 and consists of 162 modules. The array is divided into an upper and lower rooftop system. The upper level is the array of interest in this study, and is composed of 75 modules with a total nameplate power of 18.375 kW. It consists of three separate sub-arrays connected to single-phase inverters (6.125 kWp per inverter). Two of these sub-arrays use a 5 kW SolarEdge (SE5000A-US-CAN-U) and a 5 kW Kaco (BP5002 xi) inverter. The third sub-array modules are equipped with power optimizers and connected to a 5 kW SolarEdge (SE5000A-US-CAN-U) inverter. The properties of the PV array are shown in Table 2.  
The data were collected and divided into two components: electrical measurements of the array and meteorological data from the rooftop weather station. Data measurement interval depended on the instrument's response time, but most data were collected at 10-second intervals, which could then be resampled to longer periods. Meteorological data  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter Name** & **Symbol** & **Value** \\ \hline Reference Efficiency & \(\eta_{ref}\) & 15.23\% \\ Reference Temperature & \(T_{ref}\) & 25 \({}^{\circ}\)C \\ Absortivity & \(\alpha\) & 0.9 \\ Temperature Coefficient & \(\beta\) & \(-\)0.43\%/\({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the PV array.  
Figure 1: PV array setup at CanmetENERGY in Varennes.  
include: horizontal, direct, diffuse, and plane of array irradiance, humidity, atmospheric pressure, wind direction and speed, rain quantity, snow depth, ambient temperature, and upper array module temperatures using a thermocouple array (four sensors in total, attached on the backside of the array). AC and DC power are measured at the inverter level, as well as total energy produced by the system at the exit of the transformer. Table 3 lists the equipment that was used in this study.
# A Decentralized Receiver in Gaussian Interference
## 1 Introduction  
We are at a remarkable time for radio systems. With the significant reductions in cost and improvements in performance, there has been an explosion in the number of radio systems with a wide range of applications including personal, machine-to-machine, vehicle-to-vehicle, and Internet-of-Things (IoT) communications. In many situations, radios cluster in physically close groups. This has two effects. First, it increases the likelihood of interference between systems and necessitates that systems be designed to maintain good performance in these conditions. Second, it creates an opportunity for groups of independent radios to be used as a distributed array to improve performance. Considering this new setting, we study the problem of receiving a signal in the presence of interference with the help of a distributed array, as illustrated in Figure 1.  
We develop bounds on the maximum communications rate from a single transmitter to a base node that is provided side information from a distributed receive array in the presence of interference. The nodes that provide side information to the base are identified as _helpers_, and we assert they do not communicate with one another, but only forward information to the base through a previously established reliable Local Array Network (LAN) link, and that this link only supports a maximum total throughput which all the helpers must share.  
This receive system can mitigate a strong interferer in a wide variety of environments. We consider the effects of an overall LAN capability that is parameterized by a total number of bits that helpers can share with the base for each source channel usage. Furthermore, to investigate algorithmic needs as afunction of environmental conditions, we consider a spatially Ricean channel and a parametrically defined interference. Finally, we consider both the conditions in which the base node does and does not have its own noisy observation of the source signal. Our bound development in this paper provides significant extensions to and clarifications of the results found in References [1; 2], and our preliminary efforts in Reference [3].
# An Efficient Finite-Difference Stencil with High-Order Temporal Accuracy for Scalar Wave Modeling
## 3 Dispersion Analysis  
We denote the phase velocity \(v_{FD}=\omega/|\mathbf{k}|\) and then yield the phase velocity ratio  
\[\delta(\beta,\theta)=\frac{v_{FD}}{v}=\frac{1}{r\beta}\arccos(1+0.5r^{2}q( \beta,\theta)). \tag{22}\]Here, \(q\) is  
\[\begin{split}& q=a_{0,0}+2\sum_{m=1}^{M}a_{m}[\cos(m\beta\cos( \theta))+\cos(m\beta\sin(\theta))]\\ &+4\sum_{n=1}^{N-1}b_{n}[\cos(n\beta\cos(\theta))\cos(n\beta\sin( \theta))],\end{split} \tag{23}\]  
In addition, \(\beta=kh\). We analyze and compare the ratios \(\delta\) of different FD methods, and the abbreviations of these FD methods are listed in Table 1. Figure 2 shows the ratios \(\delta\) varying with parameters \(\beta\) and \(\theta\).  
It is clear that the ratios \(\delta\) of the TE-C method were far larger than one for the high-wavenumber region (Figure 2a), resulting in a serious temporal dispersion error. The corresponding ratios of the new method (Figure 2b) were significantly improved, indicating  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Abbreviations** & **FD Coefficients** & **FD Stencils** \\ \hline TE-C & TE-based FD coefficients & Cross-stencil \\ TE-CR & TE-based FD coefficients & Cross-rhombus stencil \\ TE-R & TE-based FD coefficients & Radiation stencil \\ DRP-R & DRP-based FD coefficients & Radiation stencil \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviation table of different FD methods used for dispersion analyses.  
Figure 2: Dispersion curves varying with the parameters \(\beta=kh\) and propagation angle \(\theta\). Here, \(\Delta t=0.0015\) (s), \(v=1500\) (m/s), \(h=6\) (m), \(M=8\), and \(N=8\): (**a**) TE-C method, (**b**) TE-R method and (**c**) DRP-R method.  
that the TE-R method can effectively mitigate the temporal dispersion error for the high-wavenumber components. Figure 2c shows that the DRP-R method had the smallest dispersion error compared with the other methods.  
The TE-CR method will multiply the computational cost by the increase in the temporal order \(N\). Then, we analyzed the ratios \(\delta\) of the TE-CR and TE-R methods with different orders \(N\), and the results are shown in Figure 3. It can be seen that the performance of the TE-R method was slightly less than that of the TE-CR method, but the cost of the TE-CR method increased exponentially in the high-order cases. Although the new stencil sacrificed a little approximation accuracy, it had great improvement in computational efficiency.
## 2 Materials and Methods
### Hyperspectral Image Collection  
#### 2.2.1 Image Acquisition  
The hyperspectral images of coal were collected using the HySpex series of HSI spectrometers (Norsk Elektro Optikk A/S) including VNIR-1800 with wavelengths from 405 to 995 nm and SWIR-384 with wavelengths from 953 to 2517 nm. The system is mainly composed of a mobile platform, twelve halogen lamps, and two HySpex spectrometers in which the wavelength range is divided into 186 and 288 spectral bands at 405-995 nm and 953-2517 nm, respectively. The raw image acquired by the HSI spectrometers is actually the signal intensity of the sensors. Therefore, the dark and white reference image are required to calibrate the raw image to reflectance data. During hyperspectral data acquisition, the white reference image was captured under identical conditions to the coal raw image by using a homogeneous, stable, and highly reflective white plate (MFB99-006Y, Anhui Institute of Optics and Precision Machinery, Hefei, China) as the calibration reference material. To eliminate the effects of dark current from the imaging sensors, the dark reference image  
Figure 1: Research flow chart of the study.  
was obtained with the light source turned off and the camera lens fully covered by a non-reflective opaque cover. After hyperspectral data acquisition, reflectance calibration was performed according to Equation (2):  
\[R=\frac{DN_{S}-DN_{D}}{DN_{W}-DN_{D}} \tag{2}\]  
where \(R\)\(\in\)[0,1], \(DN_{D}\) represents the dark reference image value, \(DN_{W}\) represents the white reference image value, and \(DN_{S}\) is the coal raw image value. The pseudo-color images of four randomly selected coal samples are shown in Figure 2.
## 4 Simulation and Analysis of the MRAI-LDPC Decoding Algorithm
### Decoding Performance
#### 4.1.2 Experiment 2  
We also simulated the \(F_{16}(255,175)\) regular NB-LDPC code with a finite geometric construction [22]. This code has constant column weight \(\rho=16\), row weight \(\gamma=16\), and code rate \(R=0.68\). The parameters are set as follows: (1) for the AlgB algorithm, the threshold is set to 9; (2) the threshold and the Hamming distance coefficient are set to 9 and (2.1, 2.0, 1.0, 1.0) with the wtd-AlgB algorithm; (3) the threshold \(T_{H}\) of the presented MRAI-LDPC algorithm is set to 8.  
Figure 6 depicts the decoding performance of the q-ary sum-product algorithm (QSPA) [23], AlgB decoding, wtd-AlgB algorithm, and MRAI-LDPC algorithm. Some results can be noted as follows:  
* At a BER of \(10^{-5}\), the presented MRAI-LDPC algorithm has about 0.4 dB performance gain over the wtd-AlgB algorithm. Compared with the AlgB algorithm, the presented MRAI-LDPC algorithm can achieve a performance gain of about 1.35 dB with a BER of \(10^{-4}\);
* When the SNR is greater than 4.5 dB, the BER curve of the MRAI-LDPC algorithm decreases faster, and the decreasing trend is almost the same as that of the QSPA algorithm.  
Additionally, we list the FER performance of the AlgB algorithm, the wtd-AlgB algorithm, and the MRAI-LDPC algorithm in Table 1. According to the numerical values, the FER of the MRAI-LDPC algorithm is smaller than that of the wtd-AlgB algorithm and the MRAI-LDPC algorithm under the same SNR, which indicates the presented MRAI-LDPC algorithm has the best error-correcting performance.  
As can be clearly seen in Figure 7, the presented MRAI-LDPC decoding algorithm has the fastest ANI performance, and its average number of iterations is smaller than that of the AlgB and wtd-AlgB algorithms. At an SNR of 4.4 dB, the wtd-AlgB algorithm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SNR**} & **AlgB Algorithm (FER)** & **wtd-AlgB Algorithm (FER)** & **MRAI-LDPC Algorithm (FER)** \\ \hline
4.0 dB & 0.6711409396 & 0.5602240896 & 0.2554278416 \\
4.2 dB & 0.5390835580 & 0.3241491086 & 0.1257071025 \\
4.4 dB & 0.3401360544 & 0.1877934272 & 0.0412116217 \\
4.6 dB & 0.2202643172 & 0.0714285714 & 0.0127575429 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The FER performance comparison of the \(F_{16}(255,175)\) QC-LDPC code.  
Figure 6: The decoding performance of the \(F_{16}(255,175)\) QC-LDPC code.  
requires about 22 iterations on average. In contrast, the MRAI-LDPC algorithm requires about 7, which means that the MRAI-LDPC reduces the average number of iterations by 68% compared with the wtd-AlgB algorithm.