esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parameter Identification Methods for Stations through Arch Cover Method
### The GP Respond Surface Optimized by DE  
GP algorithm is mainly determined by the mean function and covariance function. Consider regression model [16; 20; 32].  
This is example 1 of an equation:  
\[y=f(x)+\varepsilon \tag{1}\]  
where \(x\) is the input variation; \(y\) is the observed value; Noise \(\varepsilon\)-\(N\big{(}0,{\sigma_{\text{n}}}^{2}\big{)}\). The expectation and variance of the predicted value \(y^{\prime}\) can be obtained from the Bayesian posterior probability formula:  
This is example 1 of an equation:  
\[{\mu^{\prime}}_{y}=C\big{(}x^{\prime},X\big{)}\Big{[}C(X,X)+{\sigma_{\text{n}} }^{2}I_{\text{n}}\Big{]}^{-1}y \tag{2}\]  
Figure 1: Relationship among parameter identification, numerical simulation and construction scheme optimization.  
\[\sigma_{y}\,^{2}=C\big{(}x^{\prime},x^{\prime}\big{)}-C\big{(}x^{\prime},X\big{)} \Big{[}C(X,X)+\sigma_{n}{}^{2}I_{n}\Big{]}^{-1}C\big{(}X,x^{\prime}\big{)} \tag{3}\]  
In GP, the covariance function is equivalent to the kernel function, and the squared exponential covariance function is the commonly used covariance function.  
\[C\big{(}x_{i},x_{j}\big{)}=\sigma_{f}{}^{2}\exp\left[-\frac{1}{2}\big{(}x_{i}- x_{j}\big{)}^{T}M\big{(}x_{i}-x_{j}\big{)}\right]+\sigma_{n}{}^{2}\sigma^{ij} \tag{4}\]  
where: \(\sigma_{f}{}^{2}\) is the signal variance of the kernel function, \(M=diag\big{(}I^{-2}\big{)}\) is the diagonal matrix of the hyperparameter, \(I\) is the variance scale, and \(\sigma^{ij}\) is the Kronecker symbol.  
\(\theta=\Big{\{}M,\sigma_{f}{}^{2},\sigma_{n}{}^{3}\Big{\}}\) is made the hyperparameter, and the optimal hyperparameter can be obtained using the log function maximum likelihood method. The partial derivatives of the negative log-likelihood function and the hyperparameter \(\theta\) are:  
\[L(\theta)=\frac{1}{2}y^{T}C^{-1}y+\frac{1}{2}\text{lg}|C|+\frac{n}{2}\text{lg }2\pi \tag{5}\]  
\[\frac{\partial L(\theta)}{\partial\theta_{i}}=\frac{1}{2}tr\bigg{[}\Big{(} \alpha\pi^{T}-C^{-1}\Big{)}\frac{\partial C}{\partial\theta_{i}}\bigg{]} \tag{6}\]  
The DE algorithm is a differential evolution algorithm, which is also a population-based evolutionary algorithm. In the DE algorithm, the initial population is generated first, all the new individuals in the initial population are selected as the same parent probability, and better individuals are selected as the next generation between new and parent individuals. If the population generation is \(G\) and the number is \(N_{P}\), the population vector of generation \(G\) is represented as \(x_{i}(G),i=1,2,3\cdots,N_{P}\). Each vector individual contains \(D\) components. The basic process of the DE algorithm is as follows [8; 33].
# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 5 Study Cases
### BN Generation Examples
#### 5.1.1 Example 1 Sensors Monitoring: GPS Example  
A GPS receiver is used worldwide and offers a very affordable solution with limited on-board processing, in contrast to vision-based approaches. Nevertheless, the GPS system is error-prone. The accuracy and the reliability of the position given by a GPS depend on contextual factors affecting the satellite signal during its propagation or its reception. The sources of error can be identified at the system level by means of additional bias for the computation of pseudorange measurements [32; 33]. These measurements can tune the GPS positioning accuracy from slightly imprecise to completely faulty. They can be improved by introducing observations [34] or real environments [35]. The aim of the HM for GPS is to estimate the positioning deviation in terms of probabilistic values considering the context of use on the basis of sensor information. The HM of GPS can be modeled by means of BNs from the FMEA. The main GPS localization errors are shown in Figure 19a and the corresponding FMEA is represented in Table 5.  
Figure 19: **(a) Illustration of the different types of error for the GPS receiver. (b) The BN monitor generated from the FMEA-like table of Table 5.**  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Error Types** & **Monitoring** & \begin{tabular}{l} **Appearance Context** \\ **(Error Types or Monitor)** \\ \end{tabular} \\ \hline Ionosphere & Dual frequency & - Low elevation satellites \\  & measurement & - Solar storm \\  & & - Proximity to geomagnetic \\  & & equator/poles \\ \hline Troposphere & Model based on: & - Dryness \\  & - temperature & - Vapor \\  & - relative humidity & \\  & - satellite elevation angle & \\ \hline Multipath & \
\begin{tabular}{l} Obstacle detection \\ (laser sensor, software) \\ \end{tabular} & - Urban terrain \\  &
\begin{tabular}{l} (laser monitor) \\ \end{tabular} \\ \hline Receiver & Comparison with the & - High altitude \\ (internal monitor) & calculated position & - Vibration \\ \hline \hline \end{tabular}
\end{table}
Table 5: FMEA for GPS.  
The BN for GPS HM is given in Figure 19b. We can obtain the BNs from the FMEA of the GPS (Table 5) using the proposed model. The GPS localization system accuracy is mainly evaluated by the 4 error types, which themselves are monitored by software or hardware sensors as well as their appearance contexts. The sensors themselves can be faulty and their Healths are reinforced by the appearance contexts. An example for laser sensors is the weather node, which can be given by weather forecast or temperature sensors.
# Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability
## 4 Evaluation of the Proposed Method
ZigBee is a type of wireless network that accomplishes its tasks through the transmission of a signal from one piece of hardware to another. This contributes to the network becoming more robust and extensive. ZigBee can be utilized in a wide variety of applications, including door locks, thermostats, dimmer switches, and many others [32]. KNX is the third approach, and is an open-source protocol that is typically utilized for automation. Because each KNX system is intelligent and operates on more than one physical layer, the functionality of other devices connected to the network is unaffected if one of the systems fails. KNX is a building control system [33]. As can be seen in Figure 5, the packet sizes have become significantly bigger across the board. In Figure 6, in this measure, the amount of memory consumed (in megabytes) to complete authentication requests in smart buildings based on the IoT has been examined and compared based on the number of packets sent; the results indicate that the third method has the lowest amount of memory consumption for more packages, whereas the proposed method was not able to reduce memory consumption for high package counts. Figure 4: Comparison of (**a**) energy consumption reduction, and (**b**) the energy required for data transmission and reception of two normal methods and FCPSO optimization. Figure 5: Execution time with different methods. In a second test, the amount of energy required to transmit data from the source to the destination is assessed. The quantity of data packets has increased at each successive stage. Figure 7 demonstrates that the proposed method utilizes less energy than comparable methods. The accuracy criterion indicates how effectively the suggested technique can accurately display the identify in IoT-based smart buildings. In Figure 8, the accuracy of the suggested technique for 188 unique identity effects is compared to the accuracy of the second and third methods (described previously) for various packages. Figure 6: Memory required with different methods. Figure 7: The energy demand to transmit the request with different methods. The simulation results indicate that the proposed method is more accurate than the second and third methods for identifying distinct packages in IoT-based smart buildings.
## 3 Results
### Primary Endpoints
Statistically significant correlations are in bold. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**Dependent Variable**} & \multicolumn{1}{c}{**UPDRS II**} & \multicolumn{1}{c}{**UPDRS III**} & \multicolumn{1}{c}{**NMSS**} \\ \multicolumn{1}{c}{**(2019 Values)**} & \multicolumn{1}{c}{**\% Change 2019–2020**} & \multicolumn{1}{c}{**\% Change 2019–2020**} & \multicolumn{1}{c}{**\% Change 2019–2020**} & \multicolumn{1}{c}{Coeff. \(-0.543\); R2.0.010 \(p=0.17\)} \\ \hline Age & Coeff. 0.569; R2.008 \(p=0.21\) & Coeff. 0.361; R2.002 \(p=0.50\) & Coeff. \(-0.543\); R2.0.010 \(p=0.17\) \\ \hline Disease duration & Coeff. \(-0.347\); R2.002 \(p=0.49\) & Coeff. \(-13.36\); R2.0027 \(p=0.02\) & Coeff. \(-0.261\); R2.0.002 \(p=0.55\) \\ \hline BMI & Coeff. \(-0.558\); R2.0.002 \(p=0.54\) & Coeff. \(-0.731\); R2.0.003 \(p=0.48\) & Coeff. \(0.139\); R2.0.0001 \(p=0.86\) \\ \hline UPDRS II & Coeff. \(-2.42\); R2.013 \(p=0.0001\) & Coeff.\(-1.24\); R2.0.023 \(p=0.03\) & Coeff. \(-0.109\); R2.0.0001 \(p=0.80\) \\ \hline UPDRS III & Coeff. \(-0.509\); R2.0.013 \(p=0.10\) & Coeff. \(-1.84\); R2.0.128 \(p=0.0001\) & Coeff. \(0.204\); R2.0.003 \(p=0.46\) \\ \hline NMSS & Coeff. \(-0.218\); R2.0.014 \(p=0.09\) & Coeff. \(-0.084\); R2.0.002 \(p=0.58\) & Coeff. \(-0.477\); R2.0.090 \(p=0.0001\) \\ \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{Legend: BMI = Body Mass Index; Coeff = coefficient; NMSS = Non-Motor Symptoms Scale; UPDRS = Unified Parkinson’s Disease Rating Scale; val: value.} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Clinical and functional status scores collected on 221 patients with PD from 2018 to 2021. Main outcome changes at the end of each period of interest are described as a percentage of the change compared to the starting value. Statistically significant comparisons are indicated in bold. we found no significant relationship. On the other side, significant correlations were observed between the % changes in UPDRS II and UPDRS III observed in 2020-2021 with their starting values, and, more precisely, the lower the starting values, the greater their worsening during the subsequent year (Table 3).
# 3-D Printed Fabry-Perot Resonator Antenna with Paraboloid-Shape Superstrate for Wide Gain Bandwidth
## 3 Design of the Proposed FPRA with Wide Gain Bandwidth
### FPRA with a Traditional Planar Superstrate  
To provide a performance comparison, and moreover some necessary design data for the proposed FPRA, a FPRA with a traditional planar superstrate is designed first. This FPRA is also with a metallic grating superstrate. Except for the planar configuration of its superstrate, this antenna has the same structure (see Figure 3) and nearly the same parameters as that of the proposed FPRA with a paraboloid-shape superstrate.  
The unit cell of the planar metallic grating superstrate is illustrated in Figure 4a, by applying four periodic boundaries to surround the unit cell and two ports located above and below the unit cell, the reflection phase of the superstrate is calculated. Figure 4b shows the simulated reflection coefficient of the superstrate. As can be seen from Figure 4b, the reflection coefficient phase varies with frequency and its value is \(145^{\circ}\) at the working frequency 6 GHz. Then according to Equation (2), the resonant height \(H_{c}\) for the FPRA with a traditional planar superstrate is initially chosen to be 22.5 mm. To finely determine \(H_{c}\), a popular commercial software CST (computer simulation technology) microwave studio (MWS) based on the finite integration technique (FIT) is employed, and the gain against \(H_{c}\) is simulated, as shown in Figure 5. Obviously, the optimal value of \(H_{c}\) is 23 mm, which corresponds to  
Figure 3: Cross-section of the FPRA with a planar superstrate.  
Figure 2: Configuration of the proposed FPRA. (**a**) 3-D view; (**b**) cross-section view with main structural parameters.  
a peak gain of 17 dBi at the working frequency of 6 GHz. The 3-dB gain bandwidth for this PPRA is narrow as can be seen in Figure 5, only about 6% (from 5.81 GHz to 6.17 GHz).  
Figure 6 illustrates the simulated \(|S_{11}|\) and radiation pattern at 6 GHz. It can be seen from Figure 6a,b, the \(|S_{11}|<-10\) dB impedance bandwidth is 7.8% (from 5.77 GHz to 6.24 GHz) and a good radiation pattern is obtained at its working frequency of 6 GHz.
## 5 Experiment and Analysis of Results
### Analysis of Experimental Data  
#### 5.3.1 Experimental Evaluation Parameters  
To verify the effectiveness of the optimization, the DL-CAR data set is used to perform the following tests, analyze the experimental data, and compare the experimental results. The problems of missed detection and false detection will happen in the monitoring processes of traffic flows. In this experiment, Precision, Recall, and mAP are used as evaluation parameters. The \"Precision\" (accuracy) is the ratio of the number of correctly detected statistical vehicles to the total number of detected statistical vehicles. The \"Recall\" (recall rate) is the ratio of the number of correctly detected statistical vehicles to the total number of vehicles in the data set. The calculation method is shown in Equations (8)-(10).  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[AP=\frac{1}{11}\sum_{r\in(0,0.1,0.2,...,1)}x_{r},x_{r}=\max(Precision) \tag{10}\]  
In these formulas, True Positive (TP) indicates the number of correctly detected vehicles, True Negative (TN) indicates the number of correctly detected backgrounds, False Positive (FP) indicates the number of incorrect detections, and False Negative (FN) indicates the number of missed detections, respectively. In Equation (10), \(x_{r}\) is the maximum value of the recall rate greater than the corresponding precision rate of the interval segment, and then the average value of the maximum value of 11 points is calculated. In practice, we do not directly calculate the PR curve but smooth the PR curve. That is, for each point on the PR curve, the value of Precision is the value of the largest Precision to the right of that point.
## 4 Antennas for Beam Steering and Beamforming
In addition, beam-hopping techniques have been investigated as a solution to manage uneven traffic demands. These methods enable flexible adjustments in both time and power domains, ensuring that diverse communication needs are effectively met [70]. These methods highlight the importance of adequate power management to sustain dynamic beam-steering in LEO satellite systems with limited power resources. Additionally, artificial intelligence (AI) offers transformative potential by enabling real-time adaptability and optimizing antenna arrays. AI-driven solutions can enhance power allocation, minimize latency, and boost beamforming precision, ensuring optimal utilization of CubeSat energy reserves [71].
# Mechanisms of Sweep on the Performance of Transonic Centrifugal Compressor Impellers
## Appendix A Correlation between the Sweep Angle and the Induced Inlet Lean Angle  
The current sweep definition will introduce an extra inlet lean angle. The correlation between these two angles is analyzed here.  
Considering to extend the tip of the leading edge along the chord direction while maintaining the rest part unchanged, the shroud inlet tangential position of the extended case (\(\theta_{E1}\)) is deduced as follows:  
\[\theta_{E1}=\frac{r_{1t}-r_{1t}}{r_{1t}}\tan\alpha_{S}\tan\beta_{1t}+\theta_{1}, \tag{1}\]  
where \(r_{1t}\) and \(r_{1t}\) are the inlet hub and tip radius, and \(\alpha_{S}\), \(\beta_{1t}\), and \(\theta_{1}\) are the defined sweep angle, the inlet tip blade angle and the shroud inlet tangential position of the datum case, respectively. The difference between \(r_{1t}\) and \(r_{1t}\) is the inlet blade height (\(b_{1}\)).  
Considering the current sweep definition by scaling up or down the shroud chord, the scale factor can be estimated with the assumption that the inlet blade shape is linear and its slope is \(\tan\beta_{1t}\):  
\[\frac{\theta_{S1}-\theta_{2}}{\theta_{1}-\theta_{2}}=1+\frac{r_{1t}-r_{1t}}{c _{s}}\frac{\tan\alpha_{S}}{\cos\beta_{1t}}, \tag{2}\]  
where \(c_{s}\) is the shroud chord length, \(\theta_{S1}\) is the shroud inlet tangential position of the swept case, and \(\theta_{2}\) is the shroud exit tangential position of the datum case. The difference between \(\theta_{1}\) and \(\theta_{2}\) is often referred to the wrap angle. \(\theta_{S1}\) is then obtained as follows:  
\[\theta_{S1}=\frac{r_{1t}-r_{1t}}{c_{s}}\frac{\tan\alpha_{S}}{\cos\beta_{1t}}( \theta_{1}-\theta_{2})+\theta_{1}. \tag{3}\]  
The difference between \(\theta_{E1}\) and \(\theta_{S1}\) is defined as the induced inlet lean angle (\(\theta_{L1}\)). A positive value of it indicates that the blade has leaned to the rotating direction, and the opposite to the counter-rotating direction.  
\[\begin{split}\theta_{L1}&=\theta_{S1}-\theta_{E1} \\ &=(1-\frac{r_{1t}}{r_{1t}})[\frac{r_{1t}(\theta_{1}-\theta_{2})-c _{s}\sin\beta_{1t}}{c_{s}\cos\beta_{1t}}]\tan\alpha_{S}.\\ &\approx(1-\frac{r_{1t}}{r_{1t}})[\frac{r_{1t}(\theta_{1}-\theta _{2})-c_{s}\sin\beta_{1t}}{c_{s}\cos\beta_{1t}}
# Binary Decision Support Using AHP: A Model for Alternative Analysis
## 4 Case Study  
To demonstrate the applicability of the proposed Binary AHP model, its application addressed a real-world decision-making challenge encountered by a Brazilian public agency linked to the Executive Branch, specifically in the justice and public security sector. The core issue involved determining whether specific technological solutions qualified as Information and Communication Technology (ICT) solutions. This classification is crucial because it directly impacts procurement policies, regulatory compliance, and the allocation of financial and technological resources.  
The lack of transparent and standardized criteria for this classification process led to inconsistencies in decision-making, which created operational inefficiencies and administrative challenges. Different sectors within the agency often interpreted the classification differently, resulting in subjective and sometimes conflicting decisions. For example, some departments considered items such as UPS devices or multimedia kits as non-ICT solutions, while others included them under the ICT category. This variability in interpretation caused delays, increased the bureaucratic complexity, and undermined the transparency of procurement decisions.  
Currently, decision-makers in the agency rely primarily on individual judgment for ICT classification without using a structured decision support tool. As a result, the process remains susceptible to inconsistencies, inefficiencies, and potential legal uncertainties. The Binary AHP model provides a systematic and transparent framework to address these challenges by structuring decision-making criteria and reducing subjectivity in the classification process.  
We followed a structured methodology in this case study to apply the Binary AHP model. First, we defined the two opposing alternatives \"YES\" (qualifies as ICT) and \"NO\" (does not qualify as ICT) as the decision options. Next, we established the decision criteria and subcriteria based on official documents and expert consultation. We conducted pairwise comparisons using Saaty's numerical scale and calculated the priority vectors for each alternative. We then inverted the process to enhance the comparability by generating priority vectors for the alternative classification (\"NO\"). This approach allowed us to compare the decision scenarios directly and ensure a more structured and objective analysis. The following subsections detail the hierarchical structuring of the model, the pairwise comparison process, and the results obtained from the Binary AHP application.
## 2 Materials and Methods
### Radiance Measurements  
An imaging goniometer described in detail elsewhere [11] and presented in Figure 2 was used for the radiance measurements. In brief, this set-up uses a planar goniometer (arm length: 60 cm) to measure the angular and spatial values of the radiance at the surface of cylindrical light diffusers along two axes with a filtered scientific imaging camera (EM-CCD C9100-12, Hamamatsu, Hamamatsu City, Japan) fitted with a zoom lens (TV zoom lens H6 \(\times\) 2.5R-MD3; 1:1.2, Fuji Photo Optical Co., LTD, Saitama City, Japan) and a neutral density filter of T = 0.1% to avoid saturation. The detector array has 512 \(\times\) 512 pixels with pixel size of 16 \(\times\) 16 \(\upmu\)m\({}^{2}\). Images of the radiance were treated by image processing software developed in house to determine, at each angle \(\theta\), the longitudinal values of the radiance at the surface of the light diffuser [11]. Interestingly, this software also determines the value of the radiance for different azimuthal angles \(\varphi\) at the surface of the cylindrical light diffuser. Basically, the idea is to exploit the cylindrical symmetry of the diffuser to derive the radiance for different values of \(\varphi\).  
The light diffuser, which integrates a mode scrambler, was connected to a 4 watt laser diode (Ceralas(r) PDT, 652 \(\pm\) 4 nm/400 \(\upmu\)m, CeramOptec GmbH, Bonn, Germany) and positioned parallel to the axis at the center of the goniometer's rotating platform, with or without the catheter external sheathing around it. Each resulting diagram shows the measurement of the radiance as a function of the longitudinal axis (Figure 3a), polar angle \(\theta\) (Figure 3b,d, (\(x,z\))-plane) or azimuthal angle \(\varphi\) (Figure 3c,e, \(y\)-axis). It is important to note that angles close to 0\({}^{\circ}\) and 180\({}^{\circ}\) (Figure 3b,c) were inaccessible to the line of sight of the camera due to the diffuser fixation blocking off the view. In a similar way, angles close to 90\({}^{\circ}\) and 270\({}^{\circ}\) (Figure 3c,e) were inaccessible to the line of sight of the camera due to emission being  
Figure 2: Goniometer setup: rotating platform, mechanical arm with EM-CDD camera, neutral density filter and diffuser holder. The polar angle \(\theta\) (corresponding to the rotation of the camera around the vertical axis) and the azimuthal angle \(\varphi\) (obtained by measuring the radiance at different radial positions of the light diffuser image) are indicated. See [11] for a full description of the set-up.  
parallel to the diffuser surface. Further details regarding this measurement setup and procedure are beyond the purpose of this paper and are described elsewhere [11].