esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implementation of Constellation Dynamic Selection in ARAIM
### Detailed Implementation of Constellation Dynamic Selection
#### 3.2.1 Jacobian Matrix Separation  
After the signal baseband processing, the geometry matrix, also known as the Jacobian matrix has been calculated with the weighted least square calculation. The Jacobian matrix separation is given as follows.  
Figure 4: Implementation of ARAIM with constellation dynamic selection.  
For an epoch with \(m\) constellations involved, the Jacobian matrix \(\mathbf{G}\) can be rewritten as:  
\[\mathbf{G}=\left[\begin{array}{ccc}\mathbf{G}_{1}&\mathbf{T}_{1}\\ \mathbf{G}_{2}&\mathbf{T}_{2}\\ \vdots&\vdots&\vdots\\ \mathbf{G}_{m}&\mathbf{T}_{m}&\mathbf{T}_{m}\\ \end{array}\right] \tag{11}\]  
where \(\mathbf{G}_{j}\) is the line of sight (LOS) vector part of \(j\)th constellation and \(\mathbf{T}_{j}\) is the corresponding receiver clock offset part as follows  
\[\mathbf{G}_{j}=\left[\begin{matrix}x_{j}^{(1)}&y_{j}^{(1)}&z_{j}^{(1)}\\ x_{j}^{(2)}&y_{j}^{(2)}&z_{j}^{(2)}\\ \vdots&\vdots&\vdots\\ x_{j}^{(n)}&y_{j}^{(n)}&z_{j}^{(n)}\\ \end{matrix}\right] \tag{12}\]  
\[\mathbf{T}_{j}=\left[\begin{matrix}\mathbf{0}_{1\times n}^{1}&\cdots&\mathbf{ T}_{1\times n}^{j}&\cdots&\mathbf{0}_{1\times n}^{m}\\ \end{matrix}\right] \tag{13}\]  
where \(\left[\begin{matrix}x_{j}^{(i)}&y_{j}^{(i)}&z_{j}^{(i)}\\ \end{matrix}\right]\) is the line-of-sight vector of the \(i\)th satellite and \(n\) represents the total number of satellites in \(j\)th constellation. \(\mathbf{T}_{j}\) is a \(n\times m\) matrix and all elements are 0 except the \(j\)th column, whose elements are 1.  
Then the separated Jacobian matrix of \(j\)th constellation is given as:  
\[\mathbf{G}_{j}^{\prime}=\left[\begin{matrix}\mathbf{G}_{j}&\mathbf{T}_{1\times n }^{j}\\ \end{matrix}\right] \tag{14}\]
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 3 Factors Affecting the Yaw Motion
### Uncontrollable Parameters: Given Conditions
#### 3.1.3 Verification of Dynamics Model  
The predictions with Equation (10) were compared with the finite element analysis results obtained with ANSYS, a commercial finite element analysis (FEA) tool. In many previous studies, the deformation of the LM Guide with respect to the load showed a high correlation between the experiment and the simulation [5; 6; 7; 8; 9]. In the ANSYS FEA model, the balls between the block and rail of the LM Guide were represented by a bushing joint, which requires two opposing contact surfaces, described as the reference and mobile in Figure 13a. The constraint of a bushing joint was applied with vertical and horizontal stiffness values (Figure 3) between the mobile and reference surfaces. Figure 13b shows the finite element mesh for FEA.  
Platform 4 in Table 2 was selected for the simulation model, and the analysis was performed in the 20-80% range of \(FO\) values.  
The yaw-induced positioning error of the stage is represented by the difference between the x-axis coordinate differences of block two and block four, as illustrated in Figure 14. The yaw angle values from the FEM simulations and Equation (10) are listed in Table 3. With \(FO\), the yaw angle tended to increase in both the results of the simulations and the dynamics Equation (10).  
Figure 12: Zoomed yaw angle graph: settling time (\(\lambda\)) and error band.  
Figure 13: Finite element model for LM Guide: (**a**) contact surfaces of a bushing joint, and (**b**) finite element mesh.  
The difference between the predictions from the FEM model and the rigid body dynamics model may be due to the structural vibration ignored in the rigid body dynamics model. It may also be due to the difference between the bushing joint and simple spring model for the LM Guide stiffness. Even with these differences, a simpler and faster rigid body dynamics model may be a useful tool for predicting yaw error at the early stage of precision linear stage design.  
The friction coefficient and damping coefficient affect the yaw angle and vibration damping, respectively. As the friction coefficient increases, the value of the yaw angle increases, and as the damping coefficient increases, the settling time decreases. For example, in the case of FO:80%, for the friction coefficient ranging between 0.002 and 0.2, No. 4 platform's yaw angle shows about 4% variation, as shown in Table 4.  
For the damping coefficient ranging between 0.01 and 100, the settling time shows a 0.6% variation, as shown in Table 5. These two factors do not demonstrate dominant effects on the predicted yaw angle for their respective practical ranges.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Friction** & **0.002** & **0.01** & **0.02** & **0.03** & **0.2** \\ \hline Yaw angle & & & & & \\ (\(\upmu\)rad) & 5.93 & 5.94 & 5.96 & 5.98 & 6.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Convergence value of yaw angle according to friction coefficient.  
Figure 14: Schematic diagram of yaw error.
## 3 Proposed Modelling Methodology, Simulation Mechanism, and Control Strategy
### Proposed Control Strategy  
The purpose of proposed control strategy is to carry out electric power balancing between energy source power generation and home user power consumption. The C-\(u\)grid may operate to provide as much home user electricity as possible when alternative energy sources, PV power generation and BES stored power are sufficient, and decrease the power supply from the utility grid. The flowchart in Figure 10 shows the proposed control strategy for coordinate management of the PV system and BES in the C-\(u\)grid. At the beginning of the flow, required solar insolation and home user load profile data are input for C-\(u\)grid operation. PV power generation \(P_{\text{PV}}\) and home users' consumed electricity \(P_{\text{Load}}\) are then used as judgments. If \(P_{\text{PV}}>P_{\text{Load}}\) is true, the requirement of BES over-voltage protection is then checked. If state of charge (SOC) is less than upper limit, 90% here, the C-\(u\)grid controls the home users' required electricity to be supplied by \(P_{\text{PV}}\), and the remains of \(P_{\text{PV}}\) are used to charge BESs; meanwhile, there is no home user electricity from utility grid \(P_{\text{grid}}\). If the SOC is higher than the upper limit, it means that the BES units are in a fully charged state and BESs may turn to an over-voltage protection state. Once \(P_{\text{PV}}>P_{\text{Load}}\) is false (poor solar insolation condition), BES low-voltage protection may be checked, and if the SOC is less than the lower limit, 40% here, home user electricity may be supplied by limited \(P_{\text{PV}}\), and \(P_{\text{grid}}\) must thus be required to compensate insufficient PV power. Meanwhile, BES enters a low-voltage protection state and does not provide any power. When SOC is higher than lower limit, home user electricity may be supplied by limited \(P_{\text{PV}}\), \(P_{\text{grid}}\), and BES power \(P_{\text{BES}}\); meanwhile, \(P_{\text{BES}}\) output is determined by the remaining energy storage in the BES units. This proposed control strategy is finally implemented at the front end of the bi-directional DC/DC converter in Figure 6.  
Figure 9: Used real-time architecture of studied C-\(u\)grid.
# Smart Facility Management System Based on Open BIM and Augmented Reality Technology
## 4 Design of an AR-Based Smart FMS  
### Smart Facility Management BPMN  
To devise a prototype for an AR-based field support system for smart FMS based on the maintenance scenarios proposed in this paper, a BPMN with the input/output (I/O) status of the data within the process is required. BPMN is a visualized notification method for representing a business process, created to show the process time flow and the flow of messages between parts of different activities.  
Figure 7 shows the BPMN with the I/O of the COBie data required for the task categories of the aforementioned AR technology applied to daily or routine inspection scenario-based FM work progress. First, the inspector generates outputs and reviews the 3D information of the inspection site from the 'Document' sheet. The inspector then visits the inspection site, identifies the AR marker and generates the input/output attribute data of the member to be inspected from the 'Type' sheet. Site photographic information gathered from the camera module of the mobile device is configured so as to be stored and retrieved from the 'Document' sheet. Once the site inspection is completed and it has been determined that there is indeed a defect, the relevant details are recorded on the 'Issue' sheet, and the maintenance history track record of the defected member is reviewed from the 'Job' sheet. Details on the warranty are reviewed from the 'Document' sheet, and repair work then proceeds.  
The BMPN at the time of defect occurrence and receipt of a defect repair report is shown in Figure 8. In this case, as with the case of daily or routine inspections, image data from the site is stored in the 'Document' sheet, while the information on the defect area is recorded on the 'Issue' sheet. The 'Job' sheet is used to confirm the maintenance history track record, and the warranty-related information is viewed from the 'Document' sheet. The COBie datasheet is configured to be mapped to each repair work item, including the maintenance repair work that occurs afterward.  
Figure 7: Daily or routine inspection BPMN.  
Most of the 3D models and attribute data of the member subject to inspection are extracted from the BIM information and stored in the COBie Excel sheet database. At this point, the additional attribute data required includes the AR marker information, inspection date, inspection cycle and warranty duration, among other factors, which are manually input by the FM inspector after receiving relevant information from the responsible supply company. Clarifying the definition of required information and the use case is the most important aspect when applying BIM information for FM. Therefore, the proposed BPMN allows for the specific implementation of the use case for smart FM.
## 2 Simulation Methodology
### Simulation Model Description  
A flowchart summarizing the simulation steps is shown in Figure 3. ANSYS Fluent supports the Multiple Reference Frame (MRF), Sliding Mesh Model (SMM), Overset Mesh, and other methods for characterizing rotating flow. The MRF method is the most frequently used because it can show the time-averaged flow, but the analysis reliability is extremely low when rotational and non-rotational domains exist simultaneously, or when there are dependencies on the geometric angle. Thus, the SMM method was applied in this simulation due to the geometric angle dependencies caused by the radial groove patterns. Then, the species transport equations were solved, and the SST was calculated with the converged flow field. All the residual values dropped to below \(1\times 10^{-5}\) and satisfied mass conservation.  
The defined SST is also called mean residence time, blend time, or mixing time. These are the primary concerns when developing agitators or stirrs. The simulation is helpful to predict the temperature and concentration variation with time for each monitoring point. For example, the required mixing time to homogenize the solution in the stirred tank was calculated by tracking the tracer concentration with CFD [27; 28; 29]. In addition, the fluid mixing speed of the helical impeller was predicted by the CFD technique with the tracer tracking method [30].
## 4 Evaluation Methodology
### Experiment Result of Feature Selection with Permutation Decoding
#### 4.4.2 Result of F1-Measure
Figure 8 shows the average experimental results of 10-20% selection features for detection rates and F1-measure values. In the experiment in this paper, using feature selection showed better performance than using all features (Figure 8), and it showed a significant difference.
# Comparison of Image Normalization Methods for Multi-Site Deep Learning
## 4 Discussion
### Classical Normalization Methods  
The best-performing tumor segmentation network (Perc-HM) reaches a Dice of \(0.69\pm 0.01\) and is in a similar range (0.68-0.74) reported in the literature [4; 26]. There are no significant differences in segmentation performance between normalization methods when training on all data. For the other scenarios, Perc, Perc-HM and GAN-Seg performed best.  
The best-performing model for Dworak classification has a lower AUC of \(0.67\pm 0.01\) compared to [27] with an AUC of 0.82, but our data set is only a quarter of the size and is more heterogeneous. For the classification of sex and age, we could not find respective studies to compare. Classifying the sex of the patients results in a high AUC of \(0.94\pm 0.02\). Probably, since some sexual organs are visible in the images, this information might be picked up by the network.  
Compared to the segmentation task, there are fewer examples, since the whole volume is classified, while for segmentation, each voxel is assigned a label and thus contributes to the overall performance, though certainly the voxels are not independent. This could explain why we see fewer differences between the normalization methods for segmentation compared to classification.  
The performance decreases for all tasks as the size of the data set decreases, as expected. This is demonstrated by leaving out one center, and especially when training only on one center (see Figure 5). Here, the largest differences could be observed between the different normalization methods. For data from unseen centers, there is a large generalization error. One of the reasons for this error is that there are greater differences in the data acquisition parameters between centers than within one, as visible in Figure 1. Some normalization methods can better reduce these differences than others.  
In summary, for tumor segmentation, Dworak and sex classification, Perc-HM is the best method in the All and Except-One scenarios. In the Single-Center scenario, Perc-HM also performs well for sex classification, but for segmentation and Dworak classification, GAN-Seg was significantly better. For age prediction, the results are inconclusive; no method is superior in all three scenarios.
## 3 Synchronous Information Transmission Analysis
### Signal Demodulation  
Signal demodulation is realized by a demodulation circuit and DSP together. The demodulation circuit is shown in Figure 6, including the voltage divider circuit, rectifier circuit, voltage follower, envelope detection circuit, low-pass filter circuit, differential amplifier circuit, protection circuit and so on. First, the collected voltage is divided to protect the chip and device. Through rectifier circuit and voltage follower, the main circuit and subsequent demodulation circuit are isolated to reduce interference. The amplitude change of the high-frequency signal is detected via envelope detection, and the selection of time constant RC of the circuit is very important. After low-pass filtering, the differential amplifier processes the voltage so that the information characteristics of the voltage are fully displayed. \(U_{\text{ref}}\) is available from chip supply voltage \(U_{\text{o}}\). After low-pass filtering, the processed voltage enters the protection circuit. The circuit selects 2.7 V regulator tube D\(\gamma\). Q\({}_{1}\) is not conductive when the input voltage does not exceed 2.7 V. Voltage drops at both ends of GS of Q\({}_{2}\), so Q\({}_{2}\) turns on. The circuit voltage normal output. When the input voltage is greater than 2.7 V and the excess voltage is greater than the Q\({}_{1}\) PN junction conduction voltage, Q\({}_{1}\) conduction. The GS voltage across Q\({}_{2}\) is equal, so Q\({}_{2}\) does not conduct. At this time, the protection circuit is broken, which plays a protective role. After ensuring that the voltage is less than 3 V, the ADC peripheral inside DSP28335 is used to convert the data voltage \(U_{\text{data}}\). The ADC of F28335 meets the demodulation requirements in terms of accuracy and acquisition rate. Finally, the demodulation process can be completed only by judging the converted information data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**M-ary Signal**} & **Voltage Amplitude** & **Phase Angle** & **Signal** \\ \hline \multirow{8}{*}{16-ary signal} & 100\% & 0\({}^{\circ}\) & 0 \\  & 99.67\% & 9.5\({}^{\circ}\) & 1 \\  & 99.34\% & 13.3\({}^{\circ}\) & 2 \\  & 99.01\% & 16.2\({}^{\circ}\) & 3 \\  & 9
#### Editorial
###### Abstract
Research at the system level provides useful information about achievable performance by integrating adsorption TES in real contexts, such as buildings and solar systems for domestic or residential purposes. The authors of [8] investigated the theoretical limits of sorption TES, first facing the classification of thermochemical heat storages, then studying their thermodynamics. They observed that the reaction enthalpy value only varies slightly for one mole of water, thus deriving the following conclusions on the energy efficiency of closed and open TES: (i) the energy required for evaporation of water is, at least, 65% of the available energy of the reaction; (ii) the maximum theoretical energy efficiency of the system is about 1.8. Based on those results, they calculated that a perfect thermochemical heat storage system would store up to 12 times more energy compared with conventional water storage with a temperature difference of 50 \({}^{\circ}\)C, thus confirming that this solution is definitely competitive. In [9], the authors presented a numerical study on the performance of a solar domestic sorption TES module operating in seven different world locations. The authors optimized the TES configuration for the Portuguese climate and then calculated its performance by varying latitude, operating conditions and solar collector tilt angle. They proved the effectiveness of the system by implementing a dynamical model and numerical simulations and by using the reduction in the annual energy consumption of a backup heater as a benchmark. The results showed that the best performances were obtained in locations where winter and summer are clearly defined, especially locations where winters are colder, and with the inclination angles of solar collectors being larger than the local latitude, except for locations with low latitudes, where the inclination angles of solar collectors are not so relevant to the system performance. Finally, in [10] the authors proposed a novel adsorption TES system based on water and zeolite 13X for industrial applications. Their unconventional scheme was conceived so that steam production and condensation, during adsorption and desorption processes, occur in the same vacuum reactor, where the zeolite is heated and cooled. They also attempted to preliminarily assess the energy performance using numerical simulations and experiments. To this aim, they constructed a reduced-scale prototype: the limited experimental campaign produced data for the validation of the simplified model of the adsorption/desorption processes. However, the experimental campaign and the simulative activities carried out by the authors highlighted some critical issues of their system, thus indicating a lack of a suitable preliminary experimental activity, and a deep comprehension of the phenomena involved. This research received no external funding. Not applicable. The authors declare no conflict of interest.
# A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach
## 5 Discussion
### Analysis of the Results  
We put YOLO V5 to the test in a variety of settings and configurations using 460 images. A detection example is shown in Figure 16. The testing accuracy findings and experimental performance using images outside of our datasets are shown in Table 5. YOLO V5 is typically more precise than earlier iterations. Group 2 (the augmented dataset) had the greatest average accuracy, with a YOLO V5 model accuracy of 95%. There were only two detection mistakes in Group 2 when using YOLO V5. The performance of YOLO will be enhanced by the large dataset, which includes both the original images and the artificial images produced by GANs. When a deep-learning-based algorithm is trained on a sufficient and adequate dataset, overfitting issues can be reduced to a significant level. Small datasets could be a mapping barrier for neural networks trying to locate an item, although the addition of synthetic samples has several benefits. Firstly, it increases the diversity of the dataset by introducing variations and expanding the range of possible input patterns. This can help the model generalize better and perform well on unseen data. Secondly, synthetic samples can help address issues related to imbalanced classes or rare events by creating additional instances of under-represented data points. This helps to provide more balanced training data and prevent the model from being biased towards the majority class. Finally, synthetic samples can be used to supplement a limited dataset, especially in scenarios where collecting more real data is challenging, time-consuming, or expensive. As a result, adding synthetic images to the dataset along with original images enhances object identification performance.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Accuracy** & **False Detection** & **Missed Detection** \\ \hline Original & 89\% & 3 & 4 \\ Augmented & 95\% & 2 & 0 \\ \hline \end{tabular}
\end{table}
Table 5: YOLO V5 class accuracy for original and augmented datasets.
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 6 Performance Evaluation
### Results  
In the following, results are reported which are achieved under different settings and traffic load (in terms of number of platoons and platoon size). They are presented in an incremental manner, gradually moving towards smarter configurations which make the best of the capabilities of the centralized cellular radio resource orchestration enabled by the usage of Mode 3.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 3 Voice Maps in Practice
### Assessing Pathological Voices  
When a new patient presents with a voice problem, the clinician usually does not have baseline data with which to compare the current vocal status. Because voices can be so different, it is not realistic to have as baselines normative, \"reference\" voice maps based on a population. Only a comparison with the same person in good voice will be truly informative, especially for the often subtle vocal problems of voice professionals. One possibility could be to incorporate the collection of a full-range voice map as part of a regular health check-up, if only for persons that depend on their voice for their livelihood. Should they encounter voice problems later, then such a desirable reference would be available.  
While the legacy voice range profiles have proven their usefulness in assessing voice problems that affect the vocal range, the focus of most recent voice mapping studies, on the interior of the contour, has so far been on healthy voices. This has given many valuable insights into probing the mechanisms of voice production, but we are still in the beginning. The time has now become ripe for applying these techniques to the assessment of impaired voices. We know from the literature on VRPs that voice pathologies manifest themselves in very diverse ways on the contours. With vocal pathologies present, the diversity of phenomena portrayed by (filled) voice maps can only be expected to increase.