esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 2 Measurement Principle and Error Analysis
### Combined Measurement Error Model  
In the combined measurement scheme of the magnetometer and satellite measurement system, the measurement of the pitch angle and yaw angle relies only on the data provided by the satellite measurement system, which is relatively simple. However, the measurement of the roll angle depends on the combination of the magnetometer and satellite measurement system. Hence, the data and measurement accuracy are affected more. To examine this, the roll-angle error model is established in this study.  
The measurement method represented by Equation (4) is expressed in the following form:  
\[\gamma=F(h_{by},h_{bz},h_{nx},h_{ny},\theta,\psi) \tag{5}\]  
In the roll angle measurement method represented by Equation (5), in addition to the measurement error of the sensor itself and random error, the main error is derived from the hypothesis of a small angle of attack. The pitch and sideslip angles of the body are considered to be equal to the pitch and yaw angles, respectively. In other words, the pitch and yaw angles used in this method have certain errors. Here, the small angle of attack error and measurement error increment are expressed in the following form:  
\[\begin{array}{l}\delta\theta=\theta-\theta_{v}\\ \delta\psi=\psi-\psi_{v}\\ \delta h_{by}=h_{by}-h_{byv}\\ \delta h_{bz}=h_{bz}-h_{bzv}\end{array} \tag{6}\]The roll angle error in Equation (5) can be expressed as:  
\[\delta\gamma=\frac{\partial F}{\partial\psi}\partial\psi+\frac{\partial F}{ \partial\theta}\partial\theta+\frac{\partial\delta F}{\partial\delta h_{by}} \partial h_{by}+\frac{\partial F}{\partial h_{bz}}\partial h_{bz} \tag{7}\]  
where:  
\[\frac{\partial F}{\partial\psi}=\frac{\frac{\partial h}{\partial\psi}m-\frac{ \partial m}{\partial\psi}n}{a^{2}+b^{2}}\]  
\[\frac{\partial m}{\partial\psi}=\cos\psi\sin\theta h_{ny}-\sin\psi\sin\theta h _{nx}\]  
\[\frac{\partial n}{\partial\psi}=-\cos\psi h_{nx}-\sin\psi h_{ny}\]  
\[\frac{\partial F}{\partial\theta}=\frac{\frac{\partial n}{\partial\theta}m- \frac{\partial m}{\partial\theta}n}{a^{2}+b^{2}}\]  
\[\frac{\partial n}{\partial\theta}=\cos\psi\cos\theta h_{nx}+\sin\psi\cos \theta h_{ny}-\sin\theta h_{nz}\]  
\[\frac{\partial m}{\partial\theta}=0\]  
\[\frac{\partial F}{\partial h_{by}}=\frac{1}{1+w^{2}}\frac{n(v-u)}{v^{2}}\]  
\[\frac{\partial F}{\partial h_{bz}}=\frac{1}{1+w^{2}}\frac{m(v-u)}{v^{2}}\]  
\[u=nh_{by}-mht_{bz}\]  
\[v=mht_{by}+nht_{bz}\]  
\[w=\frac{u}{v}\]
## 5 Analysis of Trade-Off between Traffic Congestion and Packing Density
### Packing Density  
The actual packing density of an HIN depends on five main factors, such as:  
Figure 4: Link failure at Level-2 network of TFBN between the nodes(0,0) and (1,0). The message can be routed via the nodes(0,1) and (1,1) instead of the faulty link.  
Figure 3: Link failure at BM Level. There is a fault between the Node(0,0) and Node(1,0), preventing direct commute between them.  
How the MPC system is practically implemented from chip level to the system level.
2. Number of cores interconnected at the chip level to form a node.
3. Number of nodes interconnected at the board level to form the BM.
4. Number of BMs interconnected to form the intermediate level network at the cabinet level.
5. Number of cabinets interconnected to form an MPC system.  
Each level has different kinds of links used for the interconnection, ranging from VLSI links at the chip level to optical fiber links at the system level. So, interconnecting such a huge number of cores, nodes, BMs, and cabinets involve numerous communication links and different levels of wiring complexity at each level. The packing density helps to assess the density of the interconnected nodes at each level, and is expressed as the number of nodes per unit cost of an MPC system (Equation (3)).  
\[\textit{Packing Den.}=\frac{\#Nodes}{Static\;Cost}=\frac{\#Nodes}{Degree\times Diameter} \tag{3}\]  
Considering the node degree and diameter of different networks, the packing density of the interconnection networks have been calculated according to the derived Equation (3) and then enlisted in Table 2. For the Level-2 network, it is found that the TFBN has a greater packing density than the TESH, 2D-Mesh, and TTN networks. In fact, it is also higher than 2D-Torus, 2D-Mesh, and 3D-Mesh networks and comparable with TESH, 3D-Torus, and TTN networks in the case of the Level-3 network. However, for the Level-3 TFBN, the packing density is lower compared to 3D-Torus, TESH, and TTN networks because the node degree of the TFBN is substantially higher than the other networks, whereby the number of nodes is the same for all the networks. Even with the high node degree, the TFBN yields similar packing density to those of 3D-Torus, and TESH, and TTN because of its low diameter.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 6 Conclusions  
In this paper, we have presented new simulation results from the use of a 5G PRS-based OFDM radar for the detection of high-speed trains. Many PRS configurations have been tested, using simulated TX signal echoes and the SNR values set according to the values observed in field experiments. The PRS signals have turned out to be very suitable for I SAC, as they are highly configurable and enable proper adjustment between the sensing resources and communication tasks. The flexibility of the PRS configuration allows for adaptation to varying radio channel conditions, which is essential to maintain precision in sensing  
Figure 14: PNFR for different PRS configurations with 1 PRS resource as a function of the number of frames taken for observation for different levels of reflection gains.  
Figure 15: PNFR for different PRS configurations with 2 PRS resources as a function of the number of 5G frames taken for observation for different levels of reflection gains.  
while preserving communication efficiency. The configurations with one PRS resource turned out to be the best choice in terms of acceptable trade-off between simultaneous efficient realization of sensing and communication tasks. They occupy two times less resources than configurations with two PRS resources while performing only slightly worse in the radar simulations. Furthermore, some changes in the PRS configuration have been proposed, which allow for a very rare distribution of PRS pilots in the 5G resource grid. It has been proven that, for good radio conditions, our suggestion provides decent results in sensing precision, whereas it does not occupy a large number of resource elements, which in turn leaves more resources for communication tasks. It also turned out that slightly irregular sampling in the time of the estimated channel frequency response, caused by interchangeable use of long and short cyclic prefixes in 5G NR waveforms and the use of some dense PRS time-frequency grid configurations, does not result in significant errors in Doppler frequency measurements. In this work, only the high-speed train scenario was simulated; however, the results obtained can be generalized and have wider impacts. This establishes a fundamental basis for further research and real-world applications in the integration of radar sensing and mobile communication.
### Low-Cost Devices in High-Precision Kinematic Terrestrial Positioning
The authors suggested that an altitude-based weighting algorithm is not suitable for low-cost receivers, including smartphones, while a C/N0 weighting would be a better choice for these devices. Later, [PERSON] et al. [40], [PERSON] et al. [41], and [PERSON] et al. [9] confirmed the suitability of this processing approach. The fact that this study is based on u-blox instruments operating in the L1/E1 and L2 frequency bands, smartphones operating in the L1/E1 and L5/E5a frequency bands, as well as a geodetic instrument receiving observations at all frequencies, was a good starting point for further analysis. A valuable study on real-time kinematic positioning with smartphones was recently published by [PERSON] et al. [42]. The study used two Huawei Mate30 and two Huawei P40 smartphones with two installation modes: vehicle roof mode, in which the smartphones were mounted on the roof outside the vehicle, and dashboard mode, in which the smartphones were stabilised inside the vehicle. The authors confirmed that the installation on the vehicle roof provided better results, especially in terms of RMS (Root Mean Square) for the vertical component. Therefore, in the current experiment, the authors used the aforementioned method, i.e., the smartphone was mounted on the roof of the vehicle. As part of this brief overview of the use of low-cost devices and smartphones in kinematic terrestrial surveying, it is also worth mentioning the 2021 Google Smartphone Decimeter Challenge experiment that inspired [PERSON] et al. [43] to optimise the use of RTKLIB, specifically for measurements with Android smartphones, which highlighted the changes needed given the low quality of measurements on these platforms.
## 4 Results and Discussion  
### Results and Discussion of Geo-Environment Indicator Distribution  
#### 4.1.1 Bearing Capacity of Foundation Soils  
The bearing capacity of foundation soils in different depths showed dissimilar characteristic values, varying from 105 kpa to 280 kpa, with an increasing trend from lower to deeper layers in Rongcheng County (Figure 3).  
The bearing capacity of foundation soils for suitability for urban construction between 0-5 m, making 115 kpa and 125 kpa as the grading standards, could be divided into three grades, corresponding to very high (125-130 kpa), high (115-125 kpa), and moderate (105-115 kpa) (Table 3). The bearing capacity of foundation soils between 5-10 m, making 120 kpa, 130 kpa, and 140 kpa as the grading standards, could be divided into four grades, corresponding to very high (140-180 kpa), high (130-140 kpa), moderate (120-130 kpa), and low (110-120 kpa) (Table 3). The bearing capacity of foundation soils between 10-15 m, making 150 kpa, 170 kpa, and 190 kpa as the grading standards, could be divided into four grades, corresponding to very high (190-250 kpa), high (170-190 kpa), moderate (150-170 kpa), and low (110-150 kpa) (Table 3). The bearing capacity of foundation soils between 15-30 m, making 165 kpa, 175 kpa, and 185 kpa as the grading standards, could be divided into four grades, corresponding to very high (185-240 kpa), high (175-185 kpa), moderate (165-175 kpa), and low (155-165 kpa) (Table 3). The bearing capacity of foundation soils between 30-50 m, making 200 kpa, 210 kpa, and 220 kpa as the grading standards, could be divided into four grades, corresponding to very high (220-280 kpa), high (210-220 kpa), moderate (200-210 kpa), and low (190-200 kpa) (Table 3).  
Figure 3: \(Cont.\)Figure 3: _Cont._  
Figure 3: Spatial distributions of characteristic values of bearing capacity of foundation soils at different depths.
## 3 Case Study
### Performance of Model  
The established model is evaluated with the parameters of the collected and pre-processed dataset in the MATLAB program. After the 754 th attempt, both the validation and test sets had an upward trend for the Bayesian network. The best validation performance was thus reached at epoch 754. Figure 10 depicts the performance graph of the constructed Bayesian network.  
Figure 11 depicts the regression graph between the estimated Bayesian network model values and actual ship data. The observed determination of coefficients (R) for training, test, and the total process were 0.99977, 0.99651, and 0.99906, respectively.  
Figure 10: Performance plot of the developed network.  
Figure 9: Bayesian network model framework diagram of the implemented model.  
Non-linear regressions were presented in two distinct groups based on the input variables, ballard pull (BP), and vessel speed (V) in this case study. In the first group, power functions were determined with respect to ballard pull (BP) values, whereas in the second group, power functions were determined with respect to vessel speed (V) values.  
The power function coefficients are predicted with a 95% confidence level for the bounds. The equations of predicted non-linear regression functions with their coefficients of determination values for this case study are presented in Table 2.
## 4 Discussion  
In this paper, we presented a fully working prototype of a computer vision system designed to detect pavement distress based on orthoframes captured by a mobile mapping system. The prototype has to be tested in the appropriate transportation system analysis environment; therefore, the presently claimed technology readiness level (TRL) is four (i.e., tested in a laboratory environment).  
Further items for discussion are presented next:  
* In [2], it was claimed that detection of shadow regions in the orthoframe is a critical component of the complete pavement distress detection system. However, our current tests did not completely confirm this as the system seemed to be robust to such visual artifacts. Hard shadows from tree branches still presented a problem, however, as they resembled pavement cracks.
* Ensemble classifiers were not introduced in this work as acceptable performance was obtained without complicating the system architecture. An attempt to make the detector context sensitive, i.e., use progressive zoom where a defect was suspected in the orthoframe, was considered as a possible next step in improving detection performance especially as a countermeasure for hard, fine-detail shadows.
* Data augmentation was updated to include orthoframe segment exposure variation and apparently led to improved generalization ability of the resulting ConvNet.
* Finally, the current classifier could only be regarded as a detector since the predictions about orthoframe segments were essentially binary, whether a defect was detected or not, with the additional possibility to consider suspected defects. In the future, a more advanced segmentation feature can be implemented whereby different types of defects will have related ground truth information provided by means of manual annotation for which the corresponding software package was also developed as part of this effort. In this case, however, as was shown previously, the issue of imbalanced data will have to be solved.
# A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2
## 2 Methods  
### Research Framework  
The overall framework of the proposed PFDMOEAM is presented in Figure 1, which consists of three modules, namely DA, MOO and OSS. The DA module is the first module of PFDMOEAM, which provides quantitative analysis data for the MOO module. The initial data contains two parts, product form data and product image data, which need to be obtained by using two sub-methods of the PFIA method, namely product form representation (PFR) and key image recognition (KIR). The PFR method primarily affects the innovation of the model by the accuracy and freedom of morphological representation, and the KIR method mainly affects the precision of the model through the accuracy and comprehensiveness of image recognition. Given the importance of data quality to the model, the PFIA method is often the basis and prerequisite for ensuring a good comprehensive performance of PFDMOEAM. Firstly, in the DA module of this paper, EFA is adopted as the PFIA method to get the principal component (PC) scores, and combined with image analysis, the image evaluation mean score (IEMS) of the target adjectives is obtained. Secondly, in the MOO module, the genetic algorithm and back propagation neural network (GABP) is first used to establish the non-linear mapping relationship between PC scores and IEMSs, namely multi-objective prediction model (MOPM), which is further used as the fitness function of ISPEA2. Then the Pareto solutions are derived through ISPEA2 improved by the improved crossover operator (ICO), the improved adaptive mutation operator (IAMO) and the modification operator (MO). In ISPEA2, ICO and IAMO are used to adjust the search space dynamically, and the newly introduced MO ensures the algorithm converges to global optimum efficiently. Through these operators, ISPEA2 successfully makes up for the defects of SPEA2 and retains its superiority of diversity. Finally, in the OSS module, the MADM method mainly completes accurate multi-objective evaluation of Pareto design solutions generated in the MOO module. The evaluation results can help designers to observe the actual effect of the model in time and determine the optimized direction of parameters in main methods. Due to its characteristics comply with the objectives of OSS module, entropy-TOPSIS is adopted as the MADM method to get the optimal solution.
## 3 Methods
### The Process of the ADMTCF
#### 3.1.3 The Dynamic Change of the Adaptive Template's Size  
The dynamic change of the adaptive template's size is our practice to improve the tracking accuracy. We believe that knowing the scale of the target can improve the tracking accuracy. Therefore, there are three key points. The first key point is that the size change of the adaptive template must be based on tracking using the template that is most similar to the target. The second key point is to judge whether the size of the template is expanding or shrinking according to whether the similarity between the template and the target is improved. The third key point is that we limit the adaptive size to expand or shrink less than 20 pixels to avoid the tracking accuracy drop caused by the tracker over-tracking the background.
### Objectives and Structure of the Article  
This article aims to examine the impact of green building certifications and their material selection criteria within the context of the transition to PEDs, using a case study analysis.  
Previous studies have analyzed PED case studies by classifying the adopted solutions based on technological factors, local circumstances, and planning and implementation processes [59]. Other research has compared case studies, highlighting key success factors, including technological solutions, local constraints, and the roles of public or private entities in the realization of PEDs [60]. A collection of technical solutions and best practices for PEDs is also included in the PED implementation guidelines developed within the Making-City project [61].  
However, none of these studies have classified technical solutions based on the three distinctive PED targets: energy efficiency, renewable energy production, and energy flexibility. The main sustainability criteria found in GBRS, such as the use of materials with recycled content, the proximity of production sites to construction sites, and the presence of third-party ecological certifications, will be correlated with the technical solutions implemented to meet the three PED targets.  
This study focuses on two main research questions:  
1. What recurring technical solutions can support achieving the three PED targets (energy efficiency, renewable energy production, and energy flexibility), and which materials are most commonly used for these solutions?
2. To what extent do sustainability criteria influence the selection of materials used for these solutions? The results of this research include the following:  
1. The definition of a portfolio of technical solutions for the transition to PEDs, which can be implemented and updated over time;
2. A comparison between the materials used in energy efficiency interventions and the requirements established by the main GBRS.  
Through this analysis, the article aims to make a significant contribution to the scientific debate on PEDs by offering an operational framework to align building renovation interventions with environmental certifications in the Italian context.  
The paper is structured into four sections. In addition to this Introduction, Section 2 describes the research phases and analysis methods used to assess two PED case studies in Italy. Section 3 highlights the results of the case study analysis. Finally, Section 4 presents the conclusions in relation to the research questions and the outlined objectives, discussing the limitations and potential future developments of the study.
## 2 Materials and Methods
### Theoretical Considerations
#### 2.2.6 Comparison of IFD (with the Use of Mathcad)  
Figure 3 illustrates the IFD for all the modulated signals discussed above, depending on the modulation index \(m\)/slope \(K\)/deviation \(D\), and the normalized modulation signal power \(\overline{f^{2}(t)}\). Therefore, the blue dash-dotted line represents \(\rho_{a3x}\)--the IFD of a Double Sideband Amplitude-Modulated signal according to A3X emission class, computed with Equation (9) and depending on the modulation index \(m\); the continuous red line represents  
\(p_{\rm jje}\)--the IFD of a Single-Sideband Amplitude-Modulated signal according to JBE emission class and computed with Equation (14); the red dotted line represents \(p_{\rm g3e}\)--the IFD of a phase-modulated signal according to G3E emission class, depending on the phase modulator shape \(K_{MG}\) and computed with Equation (20); and finally, the black dashed line represents \(p_{\rm f1b}\)--the IFD of a Frequency-Modulated signal according to F1B emission class, depending on de frequency deviation ratio \(D\), and computed with Equation (24). Three values of the normalized modulation signal power \(\overline{t^{2}}(\rm t)\) were considered 0.3, 0.5, and 0.8, respectively, and thus three graphs were drawn.  
By analysing Equations (9), (14), (20) and (24) implemented in the Mathcad software and the graphs drawn in Figure 3, we can state that:  
* IFDs are constant and do not depend on the parameters of the modulating signal or of the modulation stage only for the analysed emission classes JBE and J2B;
* For small values of the standard modulator signal power, of the slope of the phase modulator for G3E and of the deviation ratio for F1B (default for small occupied bandwidths), IFD for emission classes JBE and J2B are clearly superior to all other classes. Additionally, in this case, the superiority of IFD of A3X class over those of G3E, G2B and G1B is found;
* A constant IFD of 1 for JBE and J2B could mean that we can recover the signal even if its power is equal to the noise power; thus, it is not depending on the standard modulating signal power, which gives these emission classes a special stability to disturbances;
* For the emission classes G3E, G2B G1B, and especially for F1B, F2B, IFD classes, the obtained values are much higher than those of the other classes, but only for high values of the normalized modulation signal power, and of the deviation ratio or the phase modulator slope, respectively, which rise as the modulated signal band increases.