esa-sceva/satcom-chunk-collection huggingface dataset:
# Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT
## 4 Use Case Description  
With the aim of evaluating the capability of the proposed solution in terms of measuring, collecting and transmitting data, a preliminary prototype with na FiPy was developed by using a protoboard. Some additional modifications and requirements were detected during this evaluation process. As an example, the inclusion of a potential divider to measure the voltage sensor output, as well as the necessity of increasing an additional input voltage for this sensor were considered, see Figure 7. After the preliminary assembly was tested, a first printed circuit board prototype was designed and milled to minimize the number of wires required by the system by using a computer numerical control (CNC) milling machine. In this case, the printed circuit board (PCB) design was carried out by using Fritzing [62]. It is an electronic design software including libraries for different elements. Flatcam was also used to create the GCode, finally used on the CNC machine that milled the PCB. Different versions were proposed and tested to improve different aspects of the design, mainly reducing sizes and elements required for the solution. Figure 8 shows the connection diagram schematically.  
Electronic components need to be protected from moisture, direct sun radiation, dust and other external effects that could affect their normal operation. Moreover, to avoid any interference in control signals and to isolate those components under high voltage or intensity values, two different external boxes were designed using Onshape. It is a free-to-use CAD platform in which assemblies can be created by using different items to make a more complex design. Different recent applications can be found in the specific literature [63].  
With the aim of providing power to the node, a PV solar module (6.5 W rate power) with a 2000 mAh Li-Ion Polymer battery was also included. The battery was selected to ensure system operation at night or during periods when solar radiation is not enough to provide the required energy under operating conditions. To ensure a relevant autonomy of the system, the micro-controller goes into deep sleep at night and under low radiation conditions. Figure 9 shows the prototype battery and power supply scheme.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Unit Price (Euro)** \\ \hline Mainboard: FiPy & 59.4 \\ Sensor: Irradiance & 2.45 \\ Sensor: PV DS1820 temperature sensor & 1.5 \\ Sensor: DHT temperature and humidity sensor & 0.18 \\ Sensor: Voltage sensors (DC) & 18.4 \\ Sensor: Current (DC) & 17.5 \\ Supply: PV module & 9.95 \\ Supply: Battery & 6.9 \\ Supply: DC/DC converter & 7.9 \\ Supply: DC Booster (24 V) & 0.18 \\ Outdoor enclosure and wiring & 12 \\ \hline Total cost & 136.36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PV monitoring node cost.
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 2 Related Work
The correlation of the impulsive noise or the frequency in impulsive noise arrival time are some other side information [32]. When a Gaussian source is affected with uncorrelated impulse noise, optimum system performance in terms of the signal-to-noise power ratio (SNR) can be achieved with the use of a Bayesian signal estimator. In 2013, [PERSON] proposed an optimal Bayesian estimator (OBE) explicitly for real-valued Gaussian mixture noise [33]. The method was further extended to complex signals in 2015 [34]. In our work, we analyzed the effect of GAE and OBE in DME noise reduction when the impulse noise at the received signal is estimated with a two-component Gaussian mixture model. By utilizing the design parameters of GAE and OBE, we propose pulse peak attenuators and pulse peak limiters, which could effectively reduce DME noise when compared to conventional pulse blanking methods. Though the methods are analyzed in the LDACS background, it can also be well utilized in any other applications of OFDM multi-carrier systems, such as power-line communication (PLC), asymmetric digital subscriber lines (ADSL), 4G cellular systems (UMTS-LTE), digital video broadcasting (DVBT), and wireless local area networks (Wi-Fi) in reducing impulse noise. The new methods we put forward are as follows:  
1. GAE enhanced pulse peak attenuator (GAE PPA);
2. GAE enhanced pulse peak limiter (GAE PPL);
3. Joint GAE enhanced pulse peak attenuator (Joint GAE PPA);
4. Joint GAE enhanced pulse peak limiter (Joint GAE PPL);* [5] OBE enhanced pulse peak attenuator (OBE PPA);
* [6] OBE enhanced pulse peak attenuator (OBE PPA);
* [7] Joint OBE enhanced pulse peak attenuator (Joint OBE PPA);
* [8] Joint OBE enhanced pulse peak limiter (Joint OBE PPL). All these methods include two basic operations. * [1] Identification of the subcarrier affected with DME noise: threshold-based detection is utilized for this operation. * [2] Processing of the signal from the affected subcarrier to remove DME interference: knowledge from genie aided estimation is utilized in methods 1, 2, 3 and 4 for the mentioned signal processing. The difference between these methods from the actual GAE is that, here, estimation is performed only for the subcarriers identified by the threshold-based detection. Similarly, the methods 5, 6, 7 and 8 well exploit the knowledge of optimum Bayesian estimation. The advantage of all these methods compared to pulse blanking is that instead of blanking (or leaving the information), data estimation is performed to extract the information. Another advantage of these methods in comparison with the GAE/OBE estimation is the decreased complexity, as these methods work well with parameters obtained from two-component Gaussian modeling the of the received data.
# Numerical Implementation of the Barcelona Basic Model Based on Return-Mapping Integration
## 3 Numerical Implementation
### Experiment Benchmark  
According to the compression experiment of MX-80 bentonite/quartz sand mixture completed by [PERSON] et al. [51], the ratio of MX80 bentonite/quartz sand mixture used in the experiment was 70:30, and the sample suction was adjusted by dialysis method. Three standard samples with diameters of 38 mm and heights of 10 mm were prepared in the experiment and named SO-02, SO-03, and SO-04. In order to consider the influence of the gap between the sample and the container on the compression process, sample SO-01 with a diameter of 35.13 mm and a height of 10 mm was also prepared.  
The specimens were placed in the dialysis device, as shown in Figure 2, and initially loaded 0.1 MPa pressure to ensure full contact between the specimens and the stress sensor, followed by a steam cycle to allow the specimens to reach a preset suction force. The initial dry density of samples SO-02\(\sim\)04 was 1670 kg/m\({}^{3}\), and steam circulation was used to achieve suction forces of 4.2, 12.6, and 38 MPa. Before compression, the samples were allowed to expand fully until stability under the premise of maintaining the axial load of 0.1 MPa. The initial density of sample SO-01 was 1970 kg/m\({}^{3}\), whose diameter was smaller than that in the dialysis container. After absorbing water and filling voids, the dry density of sample SO-01 was close to the initial dry density of the other three samples of 1670 kg/m\({}^{3}\). Sample size and initial suction parameters in the compression test are shown in Table 1.  
After the specimen reached the preset suction, the axial compressive stress was gradually loaded up to 50 MPa by stress control at a loading rate of 0.5 MPa/h, and then we unloaded the stress at the same rate. The change of axial displacement with time was measured automatically by the experimental system during the experiment. Through calculation, the change data of volume strain with time in the process of experimental compression recovery is obtained, and then the change data of void ratio with time can be calculated. The relation between void ratio and volumetric strain is  
\[K=\frac{1+e}{\kappa}p \tag{31}\]
## 3 Results
### Uncertainty and Cumulative Error of the Implant
Finally, the worst-case CT imaging error is 0.191 mm for the 56.852 mm long end-use implant. The average thresholding error of the volume-based segmentation is 0.602 mm, which resulted from CT Protocol 1 and 300 HU, as shown in Table 6. In addition to CT Protocol 1, this thresholding value was used to segment the SD head which was used for the implant modeling process. If the length measurement of the implant involves two boundary surfaces, then the thresholding error is \(2\times 0.602\) mm = 1.2 mm. Uncertainty of the thresholding error is 0.602 mm assuming that it is not larger with a probability of 67%. Thus, it contributes to the NLD uncertainty with a value of 0.602 mm. Consequently, the thresholding error is 0.602 mm for one boundary surface and 1.2 mm for two boundary surfaces of the implant. The worst-case error of 3D modeling is 0.0001 mm/2 = 0.00005 mm that has a NLD uncertainty of 0.00005 mm/\(\sqrt{3}\) = 0.0000289 mm. The maximum magnification error of additive manufacturing is 0.015 that occurred in the z-axis of the implant, as shown in Table 8. It contributes to the LD uncertainty with a value of 0.015/\(\sqrt{3}\) = 0.00866. The NLD uncertainty is 0.061 mm, which resulted from the standard deviation of the AM error. Finally, the worst-case error of additive manufacturing is evaluated to be 0.914 mm for the maximum length of the end-use implant. The combined uncertainty represents the positive square root of the combined variances that is expressed by the law of propagation of uncertainty. The implant error for each step of the process chain is calculated according to the following Equation (2):  
\[IE_{\text{process}}=\sqrt{\left[uP_{NLD}{}^{2}+\left(uP_{LD}\times L\right)^{2} \right]} \tag{2}\]  
where \(IE_{\text{process}}\) depicts the implant error as combined uncertainty of a process in millimeters, \(uP_{NLD}\) and \(uP_{LD}\) denote the \(NLD\) [mm] and \(LD\) uncertainty associated with the process, and \(L\) represents the maximum length of the end-use implant in millimeters. Finally, the worst-case cumulative error of the implant totals to a value of 1.71 mm for one boundary surface and 2.31 mm for two boundary surfaces. The combined standard uncertainty (\(u_{c}\)) of the end-use implant encompassing all processes is calculated for one boundary surface condition according to the following Equation (3):  
\[u_{c}\left(IE_{\text{process}}\right)=\sqrt{\left[U_{NLD}{}^{2}+\left(U_{LD} \times L\right)^{2}\right]}=\sqrt{\left[0.610^{2}+\left(0.00874\times 56.852 \right)^{2}\right]}=0.787\text{ mm} \tag{3}\]
# Design and Development of an Omnidirectional Three-Wheeled Industrial Mobile Robot Platform
## 5 Mechatronic Design
### Electronic and Electrical System  
For this section, it is necessary to determine three important parameters: torque, power, and speed. Based on the mechanical analysis presented in the previous sections, with a reduction ratio of 4:1, the minimum torque required from the stepper motors is 9.25 Nm. Regarding speed, through iterative testing with the microcontroller, it is established that the motor can reach a maximum of 300 rpm with 200 pulses per revolution.  
The selection of stepper motors for the omnidirectional mobile platform is based on several technical considerations:  
* Precision and open-loop control: Stepper motors allow for precise position control without the need for feedback systems, simplifying the control architecture.
* Ease of implementation: Their inherent ability to operate in open-loop mode reduces system complexity and facilitates maintenance and troubleshooting.
* Cost and availability: Stepper motors are widely available and cost effective, making them ideal for prototype development and industrial applications where budget constraints are important.  
These characteristics make stepper motors particularly suitable for omnidirectional mobile platforms that require precise and repeatable movements. This selection is supported by recent studies. For example, [PERSON] and [PERSON] developed an autonomous indoor navigation platform using stepper motors, emphasizing their precision and ease of control in constrained environments [31].  
Similarly, [PERSON] and [PERSON] presented a tetrahedral mobile robot with omnidirectional locomotion units, employing stepper motors to achieve precise multi-directional movements [22].  
To further justify the selection of stepper motors for the omnidirectional mobile platform, Section 5.3.1 presents a comparative analysis between stepper motors and servomotors. This comparison evaluates key aspects such as control complexity, precision, cost, and performance, reinforcing the suitability of stepper motors for the current prototype requirements.  
The following Figure 27 shows the layout of the electronic components inside the body of the omnidirectional mobile robot, including the power source assemblies, the drivers for the stepper motors and the Arduino Mega. This layout has been designed to optimize the available space, facilitate access to the electrical connectors, and minimize interference between the different modules, thus ensuring the efficient and safe operation of the system.  
Figure 27: Placement of the developed of electronic and electrical system components.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 2 Related Works
On the other hand, our work aims to be applied in the future 5G communication system, in which many emerging techniques, like the millimeter-wave and massive Multiple Input Multiple Output (MIMO), make it possible to estimate the signal parameters (such as the AOA or TOF) of multipath signals with high resolution. In addition, the analytical result of the error bound can be derived as a function of the number of Access Points (APs), RSS distributions and noise power.
## 3 Design Characteristics  
### Feed Characterization  
The reflectarray will be fed with a commercial _Skyware_ horn antenna. It is an axial corrugated horn in which the geometry's parameters cannot be measured, as its aperture is covered by a protective plastic sheet, so it is difficult to model it in the electromagnetic simulator. In order to achieve an optimum design, the real aperture illumination must be taken into account. Therefore, the feed RP was obtained by measurements in the anechoic chamber of the _High Technology and Homologation Centre (CATECHOM)_ of the University of Alcala (Figure 3(a)), and it defines \(E_{inc_{m,n}}\) in Equation (1) for the optimization process.  
For the second stage, the check of performance through electromagnetic simulation and fine adjustments, the feed has been modelled as a _Potter_ horn [17], because it provides an excellent radiation pattern with suppressed sidelobes, similar to the characteristics obtained in the measurement of the _Skyware_ antenna. The Potter antenna is composed of different cylinder and conical sections that serve as a step transition in diameter. An optimization carried out for different diameters of these sections allowed us to find a horn model that minimizes the differences with the measured RPs, especially in the region of interest (\(D_{norm}\geq-10\) dB). The comparison between the measured RP cuts and the simulated ones is shown in Figure 3(b).
## 3 Optimization Framework
### Multidisciplinary Analysis  
#### 3.3.1 Aerodynamic Analysis  
The work of aerodynamic analysis is to solve the basic equations of the three-dimensional flow to gain the pressure ratio, efficiency, mass flow, etc. of each blade row. Three-dimensional aerodynamic analysis for the compressor was evaluated by the commercial package ANSYS CFX (Version 10.0 64-bit, ANSYS: Canonsburg, PA, USA, 2010) [29]. The axial compressors have a surge margin, which can deteriorate the behavior of the centrifugal compressor. The combined compressor has no geometric adjustment and no inter-stage ventilation, so the axial stage enters a surge before the centrifugal stage at low and medium rotating speeds. Thus, although only the centrifugal stage was optimized, the optimization of the centrifugal stage was carried out in the combined compressor. The calculation model includes six rows of blades from the inlet rotor to the axial diffuser outlet. Because of a large number of blades, a periodic unit was arranged in each row of blades, as shown in Figure 4.  
A periodic multi-block grid was generated by the TurboGrid module of ANSYS CFX. The grids are made up of H-type hexahedral elements. A grid-independence study was conducted to eliminate the effect of grid resolution on the flow solutions. The computations were performed with four different meshes (very coarse, coarse, medium, and fine). Figure 5 shows the results of the mesh study. Mesh independence was evaluated by the performance parameters (adiabatic efficiency, pressure ratio,  
Figure 4: Computational fluid dynamics (CFD) computational domain of the compressor.  
Figure 3: Schematic for the centrifugal compressor blade camber law and corresponding control parameters: (**a**) impeller hub; (**b**) impeller shroud; (**c**) radial diffuser.  
and mass flow) at the design point. The difference shown for the mesh sizes is given in relation to the corresponding performance parameter evaluated for the fine mesh. When the number of the grid points is more than 0.65 million, the effect of the grid resolution on the flow solutions is small. Figure 6 shows a three-dimensional CFD mesh. The size of the centrifugal impeller and radial diffuser grids are about 0.16 million and 0.105 million, respectively. A tip clearance of 0.25 mm is included in the axial rotor and the impeller. Turbulence model \(k\)-\(\varepsilon\) was used in the calculation, and artificial dissipation was added. The inlet boundary conditions provide the total pressure, the total temperature, and the direction of air flow. The outlet boundary conditions set the average static pressure. Adiabatic non-slip was imposed on the wall boundary conditions, and a mixing-plan interface was used to connect the junction surfaces. The advection convection scheme was of a high resolution. The order of turbulence numeric was of the first order. The residual type of convergence criteria was Root Mean Square (RMS), and the residual target was \(10^{-6}\).
## 2 Results and Discussion
### Statistical Analysis
#### 2.3.1 Comparison of the Extraction Methods with the Solventless Method
When analyzing the effects obtained in the case of sesquiterpene compounds (Table 4), biased effects were found for the SLE under conditions of SLE5-2 and SLE5-3 compared to SPME (\(p\) = 0.080), indicating a higher level of sesquiterpene compounds in the extraction of the SLE method under the above conditions. No more biased or statistically significant differences were found; however, possible differences were observed in the sample using Cohen's d index. The mean level of sesquiterpene compounds did not differ at all in the SPME method compared to HD and SLE under the conditions of SLE4-1, SLE4-2, and SLE4-3 (\(d\) < 0.20). On the other hand, the other conditions for the SLE possibly showed higher levels of sesquiterpene compounds compared to the solvent-free condition (\(d\) > 0.20). Furthermore, the fourth method possibly showed a higher severity of sesquiterpene compounds under the MAE2 condition compared to SPME, while under other conditions (MAE1, MAE3, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Method with Solvents**} & \multicolumn{3}{c}{**Method without Solvent**} & \multirow{2}{*}{\(t\)} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{\(d\)**[PERSON]**} & \multirow{2}{*}{\(p\)**[PERSON]**} \\ \cline{2-2} \cline{6-7}  & & & & & & & & \\ \cline{2-2} \cline{6-7}  & **Mean** & **SD** & **Mean** & **SD** & & & \\ \hline HD1 & 2.19 & 3.61 & 0.26 & 0.11 & \(-\)1.05 & 0.373 & 0.52 & 0.273 \\ HD2 & 1.39 & 2.03 & 0.26 & 0.11 & \(-\)1.07 & 0.364 & 0.53 & 0.273 \\ SLE1-2 & 0.20 & 0.17 & 0.26 & 0.13 & 0.52 & 0.655 & 0.30 & 1.000 \\ SLE1-3 & 0.18 & 0.14 & 0.26 & 0.13 & 0.68 & 0.565 & 0.39 & 0.593 \\ SLE3-1 & 1.48 & 1.29 & 0.23 & 0.16 & \(-\)1.21 & 0.439 & 0.86 & 0.180 \\ SLE3-2 & 1.40 & 1.48 & 0.23 & 0.16 & \(-\)1.01 & 0.497 & 0.71 & 0.180 \\ SLE3-3 & 1.41 & 1.55 & 0.23 & 0.16 & \(-\)0.98 & 0.508 & 0.69 & 0.655 \\ MAE2 & 9.54 & 13.04 & 0.23 & 0.16 & \(-\)1.00 & 0.501 & 0.71 & 0.655 \\ \hline \hline \end{tabular} \(t\)—Student’s t-test result. \(p\)—significance level. \(d\) [PERSON]—[PERSON]’s average deviation. \(p\) [PERSON]—[PERSON]’s average deviation.  
\end{table} 
Table 6: Analysis of the differences between the solventless method and the individual extraction methods among sesquiterpenoid compounds.
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 6 Conclusions and Outlook  
In this paper, we studied the problem of phase estimation for coherent states in the presence of unavoidable outlier quantum states. Measurement outcomes were then distributed according to a convex mixture of the ideal distribution and noisy distributions due to outliers. To overcome this problem, the methodology of M-estimators was introduced based on the well-established theory of robust statistics. Two specific M-estimators, bisquare and gamma, were studied to illustrate the advantage over conventional estimators. We found that the gamma M-estimator is most accurate as well as robust against the effect of outlier quantum states.  
Before closing our paper, we would like to make a few remarks regarding future works. Our formalism applies to more general estimation settings with a quantum gaussian state, such as squeezed states and entangled states. This is done by replacing the ideal state, a coherent state, by other states of interest in Equation (28). More general quantum contaminated models can be treated without any modification. Preliminary studies showed that M-estimators work efficiently in this case as well. Our quantum statistical model in the presence of outlier quantum states can also be used to model imperfections in other elements of quantum information processing, such as gate operations and measurement processes. In this sense, the proposed method of M-estimators has wider applications to overcome certain types of SPAM errors in practice. Last, we mainly considered a quantum communication scenario in this paper. Utilization of robust statistics should be relevant to handle the problem of outlier quantum states when implementing other high precision measurement schemes based on quantum resources, such as quantum metrology, quantum imaging, and quantum sensing.  
Conceptualization, [PERSON] and [PERSON]; software, [PERSON]; formal analysis, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript. The work is partly supported by JSPS KAKENHI grant number JP17K05571. The authors declare no conflict of interest.  
Figure 7: \(\epsilon\)-curve for phase estimation for the sample size \(n=5000\).