esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Machine Learning and Fault Diagnosis
### Ensemble Learning Algorithms
#### 3.6.2 Random Forest (RF) Algorithm  
The RF algorithm is a classification and regression algorithm whose learning is based on the decision tree. This algorithm was first proposed in 2001 by [PERSON] [116; 117]. Furthermore, the RF algorithm is considered a black box that combines several randomly constructed decision trees in parallel [115]. This model is easy to evaluate, robust to outliers, capable of handling efficient prediction on a large dataset without removing variables, and does not need cross-validation. In addition, the algorithm is presented as a reference in Machine Learning competitions and is more efficient than decision trees [112]. The disadvantages of this algorithm lie in the fact that it is not easy to interpret (explain how the forecast is calculated) since it is obtained from a large number of trees which are very deep, it is also difficult to improve because it is considered a black box and trains slowly [118]. Random forests are tree ensemble methods, they aggregate the predictors of several trees, each of which is trained separately [28]. It offers RF to detect line-to-line faults, degradation, open circuits and partial shading. Random forests, thanks to the bootstrap resampling technique (sampling technique with replacement which gives the selection procedure the particularity of being random) can repeatedly extract n different samples from a set of data. This technique allows the creation of a new training sample for training the decision tree in order to generate n decision tree classifiers [28]. Therefore, [PERSON] et al., proposed the adaptive period electrical partition (AEPP) and random drill algorithm to detect the open-circuit fault in a multilevel NPC inverter. A comparison of the results obtained with traditional methods shows a good accuracy of 99.21% and 99.38%, respectively [119]. The construction of random forests is conducted in four steps as shown in Figure 16.  
Several fault diagnosis methods based on the random forest algorithm are proposed in the literature [5; 115; 120]. A proposed RF model-based approach to detect the open-circuit fault of an inverter using the output current and voltage of the inverter gave an accuracy of about 96% [103].
# The Power System and Microgrid Protection--A Review
## 2 DG and Microgrid Challenges  
Despite the numerous advantages of the presence of DGs or microgrids in the power system from an environmental and economic point of view, there are also negative impacts on the power system. The major challenge of distributed sources is their effects on the protection system. The presence of a DG changes the structure and the electrical parameters in the distribution network. Traditional structures of distribution networks are radial and, therefore, the protection system is designed for a radial scheme. Adding a DG leads to a change in the direction of the current and looped structure in the distribution system. Additionally, fault current levels may change by embedding a DG [42]. This may lead to a loss of protection coordination. Consequently, the conventional protection schemes are not sufficient to protect power systems [43; 44]. In addition, the distribution networks with the high penetration of DG are operated connected and isolated from the grid. Reference [43] proposed the hardware-in-the-loop (HIL) adaptive protection scheme (APS) to solve the protection challenges of DGs. This method is based on the optimal calculation of relay setting groups with online self-adjustment. A novel method has been presented in Reference [44] to detect faults with HIFs.  
Reference [45] discussed various protection challenges and schemes in microgrids. The microgrid should be operated in both grid-connected and islanded modes, and the protection system should be able to disconnect the microgrid from the grid as fast as possible. The adaptive protection scheme with digital relays and advanced communication is the most successful scheme of microgrid protection.
## 3 Results
### Feature Importance Analysis for the Entire Dataset and Individual Case  
Feature importance analysis was performed for the entire dataset and individual cases, as shown in Figure 2. The overall feature importance indicated that age, CSO-PVS, hypertension, and trauma cause were significant risk factors for de novo hygroma (Figure 2A). Individual feature importance analysis for a 66-year-old man with underlying hypertension and CSO-PVS due to a slipping-down accident (Figure 2B) showed that age, CSO-PVS, and underlying disease positively impacted the de novo hygroma status. Cohort feature importance analysis of sex is shown in Figure 2C. The tendency is the same all groups, except for sex. Figure 2D shows another cohort feature importance analysis for CSO-PVS. Age and CSO-PVS severity were shown to be strongly important risk factors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Category** &
\begin{tabular}{c} **Mean \(\pm\) STD or \(n\)** \\ **(\%)** \\ \end{tabular} & \(p\)**-Value \({}^{\ddagger}\)** \\ \hline Age & years & 50.55 \(\pm\) 18.82 & \textless{}0.001 \\ \hline \multirow{3}{*}{Sex} & Male & 142 (63.96\%) & \multirow{3}{*}{0.015} \\ \cline{2-3}  & Female & 80 (36.04\%) & \\ \hline \multirow{7}{*}{Trauma cause} & Slip down & 66 (29.73\%) & \multirow{3}{*}{0.815} \\ \cline{2-3}  & In-car TA & 41 (18.47\%) & \\ \cline{2-3}  & Bicycle TA & 34 (15.32\%) & \\ \cline{2-3}  & Hitting & 24 (10.81\%) & \\ \cline{2-3}  & Out-car TA & 22 (9.91\%) & \\ \cline{2-3}  & Fall down & 21 (9.46\%) & \\ \cline{2-3}  & Syncope & 14 (6.30\%) & \\ \hline \multirow{3}{*}{Hypertension, \(n\) (\%)} & Yes & 56 (25.23\%) & \multirow{3}{*}{0.116} \\ \cline{2-3}  & No & 166 (74.77\%) & \\ \hline \multirow{3}{*}{Diabetes, \(n\) (\%)} & Yes & 35 (15.77\%) & \multirow{3}{*}{0.001} \\ \cline{2-3}  & No & 187 (84.23\%) & \\ \hline \multirow{3}{*}{CSO-PVS, \(n\) (\%)} & High-degree & 91 (40.99\%) & \multirow{3}{*}{\textless{}0.001} \\ \cline{2-3}  & Low-degree & 131 (59.01\%) & \\ \hline \multirow{3}{*}{Hygroma \({}^{\star}\)} & Development & 54 (24.32\%) & \multirow{3}{*}{-} \\ \cline{2-3}  & Non-development & 168 (75.68\%) & \\ \hline \hline \end{tabular} Abbreviations: STD, standard deviation; TA, traffic accident; CSO-PVS, centrum semivoxale perivascular space. Note: Symbol, \({}^{\star}\) is the target variable and \({}^{\ddagger}\) was performed using an analysis of variance (ANOVA).  
\end{table}
Table 1: Analysis of demographics for the development of de novo subdural fluid collection in patients with mild traumatic brain injury.
### History and Support  
From its inception in 1989, COMET has sought to advance geoscience workforce expertise worldwide by offering on-demand training solutions [2]; in the program's 30 years, this fundamental focus has not changed [1; 3]. COMET's core staff of 24 individuals includes a team of instructional designers, scientists/meteorologists, a graphic artist, multimedia developer, and information technology and administrative professionals working flexibly and efficiently to allow effective use of program funds. COMET's efforts to develop satellite and remote sensing distance learning began in the mid-1990s, with the introduction of digital satellite imagery and the launches of new generation geostationary (GOES I-M/8-12) and polar-orbiting (NOAA-KLM/NOAA-15, 16; 17) satellite series. As new products and visualization capabilities emerged, COMET's training deliverables expanded to address a range of fundamental remote sensing topics, helping forecasters and others acquire a working understanding of the underlying science. These foundational training requirements were considered vital, as they ensured forecasters' preparedness to take advantage of the new observational data products. The overall goal of the training was to increase both the adoption and successful use of products being developed as the new instruments came online.  
Figure 1: Map showing daily access to MetEd by country. MetEd serves over 190 countries and territories worldwide, with approximately 600,000 registered users as of April 2019.  
The US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) and National Weather Service (NWS), the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and the Meteorological Service of Canada support satellite training efforts at COMET by providing direct funding as well as subject matter expertise. COMET develops and delivers training at the request of these program sponsors to meet the training needs identified for their workforces. COMET currently focuses on developing web-based materials that highlight the capabilities and applications of current generation geostationary and polar-orbiting satellites, including the US' GOES-R+ and JPSS series operated by NOAA, the complementary European MSG and Metop series operated by EUMETSAT, the Japan Meteorological Agency's Himawari-8, and the COSMIC radio occultation constellation jointly operated by Taiwan and the US.
## 2 Methods
### Analytical Model of the Normal Air-Gap Magnetic Field
#### 2.1.4 Model of the Magnetic Field in the Bridge Regions  
The effect of the bridge saturation will affect the armature reactive magnetic field, which is essentially due to the magnetic voltage drop caused by the armature reactive magnetic field passing through the bridge of the rotor. The subdomain method combined with the equivalent magnetic circuit method is used to calculate the magnetic field in the bridge regions. The motor is divided into three subdomains: stator, air-gap and rotor. The interface conditions of each subdomain are as follows: the tangential air-gap flux density at the interface between the air-gap and stator is 0; the scalar magnetic potential at the interface between the air-gap and rotor is continuous; the air-gap flux density at the interface between the rotor domain and air-gap domain is continuous. The constraint equations of the undetermined coefficient are obtained as  
\[\begin{bmatrix}\textbf{K}_{11}&\textbf{K}_{12}&\textbf{K}_{13}&\textbf{K}_{14 }&\textbf{K}_{15}\\ \textbf{K}_{21}&\textbf{K}_{22}&0&0&0\\ 0&0&\textbf{K}_{33}&\textbf{K}_{34}&0\\ \textbf{K}_{41}&\textbf{K}_{42}&0&0&\textbf{K}_{45}\\ 0&0&\textbf{K}_{53}&\textbf{K}_{54}&\textbf{K}_{55}\end{bmatrix}\begin{bmatrix} \Omega_{r}\\ \textbf{A}_{gbk}\\ \textbf{B}_{gbk}\\ \textbf{C}_{gbk}\\ \textbf{D}_{gbk}\end{bmatrix}=\begin{bmatrix}\textbf{Y}\\ 0\\ 0\\ 0\\ 0\end{bmatrix} \tag{16}\]  
where \(\textbf{K}_{11}\)-\(\textbf{K}_{55}\) depend on the structural parameters of the motor, and \(\textbf{Y}\) depends on the flux leakage in the bridge regions. \(\textbf{A}_{gkh}\), \(\textbf{B}_{gkh}\), \(\textbf{C}_{gbk}\) and \(\textbf{D}_{gbk}\) are the undetermined Fourier series coefficients of the air-gap magnetic general solution, respectively [19; 20]. By solving Equation (16), the undetermined coefficients can be calculated. Further, the radial component and tangential component of the air-gap flux density of the rotor magnetic field in the bridge regions of the IPMSM can be found. \(B_{rhg}\) and \(B_{thg}\) can be obtained, whose Fourier expressions are the same as those of Equations (11) and (12).  
By superimposing the air-gap magnetic field under no-load conditions, armature reaction magnetic field and rotor magnetic field in the bridge regions, the analytical model of the normal air-gap magnetic field can be described as  
\[B_{r} =B_{rpg}+B_{rag}+(B_{rbg}+B_{rpg}) \tag{17}\] \[B_{t} =B_{tpg}+B_{tag}+(B_{tpg}+B_{tpg}) \tag{18}\]
## 4 LoRa Traffic Generator Development and Validation
Out of all the gathered results of the experiment, only approximately 4230 were received and recorded by the GW. We can conclude that approximately 30% were lost, due to collisions occurring at the receiving side. Upon closer inspection, it is revealed that the packets that have been lost are precisely the ones which have been sent at the exact same time and used the same SF notwithstanding payload dimensions. We need to mention that this evaluation setup corresponds to a pessimistic scenario that does not consider the geographical duty-cycle restriction that shows that a node must respect amongst many other node class restrictions. The emulated scenario corresponds to a high-density LoRa communication architecture that integrated thousands of nodes. The simulated results presented in Section 3 do not take into account the constraints in the hardware implementation of the LoRa devices. The simulated results are empirically generated and do not consider the hardware implementation limitations and assume that the transmitter and receiver are synchronized. Once an experimental setup is implemented, all these assumptions are no longer valid, since the hardware implementation of the GW module and of the LoRa sensor lead to results that differ from those determined on an empiric basis. The scenario that involves the generation of a high-volume data packets by the LoRa traffic generator is a pessimistic one, due to the fact that it takes into consideration a boundary condition by tackling the duty-cycle restrictions associated with the LoRa communication in some geographical areas. The synchronization between the LoRa sensor and the GW module is crucial for the correct demodulation of the received packets [36]. The action of generating packets by the LoRa traffic generator determines the apparition of collisions that lead to some of those packets not being received by the GW module. Thus, performance drastically drops. The synchronization between receiver and transmitter is done by eight preamble symbols with the packet not being received when these symbols are corrupted, due to a collision [37]. Based on this observation, the sharp loss in performance in a LoRa communication architecture is caused by the occurred collisions. Figure 14: LoRa traffic generator results. However, in a high-density large-scale type of communication architectures (where sensors are spread across a large geographical area), this pessimistic analyzed scenario corresponds to a scenario of mass-deployment. These analyses are necessary for the evaluation of scalability of the LoRa technology, so that whoever implements and develops such type of LoRa networks can take into account aspects that show the possibility of eventual collisions occurring with performance loss as a direct result.
## 4 Initial Conditions and Numerical Results
### Numerical Results
#### 4.2.2 Sensitivity of the Infiltration Parameters in the Model  
The final infiltration parameters for each case were set, as follows: \(1/4\mathrm{i}_{\mathrm{f}}\), \(1/2\mathrm{i}_{\mathrm{f}}\), \(3/4\mathrm{i}_{\mathrm{f}}\), and \(\mathrm{i}_{\mathrm{f}}\); the results were obtained after 360 min (6 h). The results show that water propagation in the catchment is more significant when the final infiltration value (\(\mathrm{i}_{\mathrm{f}}\)) is high (Figure 8A). A significant quantity of the water is also flowing on the surface and toward the outlet. When the final infiltration decreases (Figure 8B-D), a large volume of water stagnates on the surface, forming small lakes where the maximum height can reach 1 m, while a very small percentage runs off to the outlet.  
Figure 6: Water-height propagation at different time steps for a rainfall intensity of 85 mm/h.  
The water height and flow rates at the outlet corresponding to each final infiltration parameter are shown in Figure 9a,b, respectively. From Figure 9a, it can be seen that there is a significant difference between the curve obtained with i\({}_{\text{f}}\) and the other curves. This shows that the maximum value of the water level is 1 m after a simulation of 11 h for (i\({}_{\text{f}}\)), while the other curves tend toward 4.28 \(\times\) 10\({}^{-3}\) m. It should be noted that at the beginning of the simulation, i.e., at T = 1 min, the other curves (1/4i\({}_{\text{f}}\), 1/2i\({}_{\text{f}}\), and 3/4i\({}_{\text{f}}\)) reach their maximum values (0.2 m); then, at the same time, the i\({}_{\text{f}}\) curve is 0.13 m. Over a simulation time of 14 min, i.e., ranging from 0.0 to 17 min, the other curves (1/4i\({}_{\text{f}}\), 1/2i\({}_{\text{f}}\), and 3/4i\({}_{\text{f}}\)) remain above the curve (i\({}_{\text{f}}\)) before reversing proportionally from 17 min until the 11-hour point. This scenario supports the theory that the majority of the water stagnates on the surface and very little moves toward the outlet, which will lower the height of the water at the outlet. This same explanation is also reflected in the calculation of the rating curves in  
Figure 8: Water height propagation map after 6 h, with 15 mm/h as the rainfall intensity.  
Figure 7: Water height (**a**) and discharge (**b**) at the outlet.  
Figure 9b; the maximum flow (2.59 m\({}^{3}\)/s) is obtained with (i\({}_{t}\)) while the rest of the curves converge toward 4.8 \(\times\) 10\({}^{-2}\) m\({}^{3}\)/s, over a total water height at the outlet of 2 m.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 2 Ambisonic Rendering  
### Encoding  
A sound source can be encoded into Ambisonic format for a given location of azimuth \(\theta\) and elevation \(\phi\) with Ambisonic order \(M\) through multiplication with three-dimensional full normalised (N3D) SH functions \(Y\), defined as  
\[Y_{nm}^{\sigma}(\theta,\phi)=\sqrt{(2m+1)(2-\delta_{n,0})\frac{(m-n)!}{(m+n)!} }P_{mn}(\sin\phi)\times\left\{\begin{array}{ll}\cos(n\theta),&\mbox{if }\;\sigma=+1\\ \sin(n\theta),&\mbox{if }\;\sigma=-1\end{array}\right., \tag{1}\]  
where \(\sigma=\pm 1\), \(P_{nm}(\sin\phi)\) are the associated Legendre functions of order \(m\) and degree \(n\), and \(\delta_{n,0}\) is the Kronecker delta function,  
\[\delta_{n,0}\equiv\left\{\begin{array}{ll}1,&\mbox{for }\;n=0\\ 0,&\mbox{for }\;n\
eq 0\end{array}\right.. \tag{2}\]  
The total number of SH channels in an Ambisonic encoded sound field is calculated as \(K=(M+1)^{2}\).
## 1 Introduction
### Related Works
In photon-counting VLC systems, especially in situations where the received signal intensity is weak, this correlation becomes notably significant. [PERSON] et al. [5] proposed an alternating optimization algorithm based on the MMSE criterion for photon-counting MIMO ultraviolet (UV) light systems. This algorithm achieves the joint design of precoders and equalizers. [PERSON] et al. [23] proposed an MMSE receiver to mitigate MUI in a Poisson channel-based multi-user indoor communication system employing UV light. They employed a second-order cone program to create a downlink beamformer. To the best of our knowledge, there has not yet been a specific analysis regarding the sum rates of multi-user, photon-counting MIMO (MU-PhC-MIMO) VLC systems affected by Poisson shot noises. To address this gap, we examine the weighted sum rate of MU-PhC-MIMO VLC systems in the presence of Poisson shot noises and devise a novel ZF-based precoding algorithm to optimize the weighted sum rate.
# Design of a Computed Tomography Automation Architecture
## 5 Industrial Applications
### Industrial Robots
#### 5.3.2 Robot Loading/Unloading  
Automation of loading and unloading of parts is carried out with robots mainly for cabinet CT systems. ZEISS (Figure 6) is one of several companies that have commercialized this setup of both the robot and cabinet CT in combination to automate the loading and unloading of parts. Table 1 lists several popular commercially available industrial X-ray CT systems that incorporate robotic automation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Company** & **Automated System** \\ \hline Carl Zeiss AG [46] & VoluMax in-line CT inspection, Automated loading \\ \hline YXLON International GmbH [31] & Robotic system for industrial X-ray inspection of turbine blades \\ \hline GE Sensing \& Inspection Technologies [47] & SpeedScan CT 64, High-speed CT scanner and detector system \\ \hline Nikon Metrology NV [48] & Inline production CT—100\% part inspection \\ \hline North Star Imaging Inc. [49] & robotiX, Robotic automated X-ray CT scanning system \\ \hline VisiConsult X-ray Systems \& Solutions GmbH & XRHRobotStar, Automated defect recognition capable robot-system \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of commercially available industrial X-ray CT systems with robotic automation.  
The Advanced Photon Source at Argonne National Laboratory is a high-energy (7 GeV) X-ray synchrotron source that has tested various high-precision positioning techniques [51]. One of these included a robot-based sample-exchange automation system with high positioning repeatability for X-ray cryo-biocrystallography. This robot was used for the changing and realigning of mounted samples.
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 5 Signal Processing Framework
### Multi-Tag Separation and Communication Processing  
When there are multiple tags and objects in the view, each tag is assigned an individual signature code. The received down-converted signal can be represented as (10). The down-converted signal \(r(t)\) needs to first be correlated with each signature code to increase the SNR and SIR. The distance between tags and the radar is random, and the time delay between signals from different tags is also random. In this case, the correlation between two different signature codes is represented as  
\[\int_{t=0}^{tT_{sym}}d_{1}(t)\times d_{2}(t-\Delta t)dt=f(\Delta t)\ll L \tag{18}\]  
where \(\Delta t\
eq 0\) is the time delay difference between two signals from two tags. After the correlation, there is still a little power of the signal from another tag left. Assume there are two tags. \(TX_{1}\) and \(TX_{2}\) are the signals from two tags. \(RX_{1}\) and \(RX_{2}\) are two correlation results on the radar,  
\[RX_{1}=a_{1}TX_{1}\times e^{i(\theta_{1}+\Delta\omega_{1}t)}+b_{1}TX_{2}\times e ^{i(\theta_{2}+\Delta\omega_{2}t)} \tag{19a}\] \[RX_{2}=a_{2}TX_{1}\times e^{i(\theta_{1}+\Delta\omega_{1}t)}+b_{2}TX_{2}\times e ^{i(\theta_{2}+\Delta\omega_{2}t)}. \tag{19b}\]  
where \(a_{1}\gg b_{1}\), \(a_{2}\ll b_{2}\), which means in \(RX_{1}\), \(TX_{1}\) has the majority of the power, while in \(RX_{2}\), \(TX_{2}\) has the majority of the power, as Figure 5 shows.  
As a result, the offset frequency \(\Delta\omega_{1}\) and phase offset \(\theta_{1}\) can be estimated from \(RX_{1}\). Similarly, \(\Delta\omega_{2}\) and \(\theta_{2}\) can be estimated from \(RX_{2}\). The offset frequency estimation has two steps. First, apply FFT to \(RX_{1}\) and \(RX_{2}\) to get a coarse frequency estimation. Second, a phase-locked loop is applied to further track the frequency and phase offset.  
After removing the frequency offset from \(RX_{1}\) and \(RX_{2}\), they become  
\[RX_{1}{}^{\prime}=RX_{1}\times e^{-j(\theta_{1}+\Delta\omega_{1}t)}=a_{1}TX_{ 1}+b_{1}TX_{2}e^{j[\theta_{2}-\theta_{1}+(\Delta\omega_{2}-\Delta\omega_{1})t]} \tag{20a}\] \[RX_{2}{}^{\prime}=RX_{2}\times e^{-j(\theta_{2}+\Delta\omega_{2}t)}=a_{2}TX_{ 1}e^{-j[\theta_{2}-\theta_{1}+(\Delta\omega_{2}-\Delta\omega_{1})t]}+b_{2}TX_ {2} \tag{20b}\]  
Simulated constellation diagrams of \(RX_{1}{}^{\prime}\) with different \(\Delta\omega_{2}-\Delta\omega_{1}\) are shown in Figure 6.