esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In conclusion, we demonstrate a contactless HR/RR monitoring system based on an indirect ToF camera. The active illumination from an 850 nm VCSEL combined with the depth information from the ToF camera can be used to compensate for the motion artifacts that commonly dominate the underlying heartbeat signal. We show that the depth-based compensation increases success rate (defined as the HR reading within 10% of the reference HR measurement) to 75%, as compared to 35% when no depth compensation method is used. The additional depth information also enables the direct measurement of RR from a participant by measuring the chest wall movement during breathing. The RR measurement from our system shows a mean deviation of 1.7 BPM compared to the reference RR reading. Furthermore, the infrared active illumination makes this system less affected by factors such as environmental lighting or the skin color of the user. In our pilot study with 29 participants, we show that our system performs similarly among participants of different demographics such as skin color, age, and gender. In the future, this system could be found useful in fields such as in-cabin driver monitoring and remote patient monitoring where non-contact unobtrusive and environmentally agnostic physiological monitoring is preferred [19; 23; 40; 41].  
Conceptualization, K.G., M.N.I. and T.Z.; methodology, K.G., T.Z. and C.J.V.; software, K.G., T.Z. and A.A.; validation, K.G. and E.P.; data analysis, K.G.; data curation, K.G., C.J.V. and E.P.; writing--original draft preparation, K.G.; writing--review and editing, K.G., E.P. and M.N.I.; visualization, K.G.; supervision, M.N.I.; project administration, M.N.I.; funding acquisition, M.N.I. All authors have read and agreed to the published version of the manuscript.  
This study received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the University of Michigan (protocol code HUM00191000, 3 March 2021).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns as well as regulation from the Institutional Review Board.  
The authors of this study thank [PERSON], [PERSON], and [PERSON] for their support in data collection, study design, and data analysis.  
[PERSON] is the founder and chief technology officer of Omni Science Inc.
## 3 Results and Discussion
### Transport Numbers in SZY Membranes
#### 3.2.1 Transport Numbers of Oxygen Ions in the Bulk and Film SZY Membranes
As can be seen, the effective conductivity of the film is about one order of magnitude smaller than that of the bulk membrane. One can assume that the difference is caused by a larger contribution of the grain boundary resistance of the film because of the smaller grain size compared to the bulk sample. It is known that in oxygen-ion conducting solid electrolytes, ion transport across the grain boundary  
Figure 5: Impedance spectra of the bulk SZY membrane exposed to \(p\)O\({}_{2}\)(air)/\(p\)O\({}_{2}\)(Ar) gradient. \(p\)H\({}_{2}\)O(air) = \(p\)H\({}_{2}\)O(Ar) = 3365 Pa.  
regions is hindered because of oxygen vacancy accumulation and formation of a positively charged core [4; 36; 37; 38; 39]. The activation energy of the effective conductivity of the film (98 eV) is somewhat higher than that of the bulk membrane (85 eV) that also indicates the larger contribution of grain boundaries typically possessing higher activation barriers in solid oxide electrolytes [40]. However, an accurate separation of the bulk and grain boundary resistances for the film membrane from the impedance spectra was hindered because of the instrument limitations. Temperature dependences of the effective transport numbers of the ceramic and film membranes in the air/argon and air/hydrogen concentration cells calculated using Equation (5) are shown in Figure 3:0 As can be seen, the effective transport numbers of oxygen ions increase with the decreasing temperature approaching the values of 0.99 and 0.97 for the bulk and film SZY membranes exposed to \(p\)O\({}_{2}\)(air)/\(p\)O\({}_{2}\)(H\({}_{2}\)) gradient, respectively, at 550 \({}^{\circ}\)C. In the air/argon cell the ionic transport numbers are lower, and the difference rises with the temperature increase, which is caused by increasing hole conductivity in oxidizing atmospheres. Figure 3:1 Temperature dependences of the effective conductivity of the bulk and film SZY membranes exposed to \(p\)O\({}_{2}\)(air)/\(p\)O\({}_{2}\)(H\({}_{2}\)) and \(p\)O\({}_{2}\)(air)/\(p\)O\({}_{2}\)(Ar) gradients. Figure 3:2 Temperature dependences of the effective transport number of oxygen ions in the bulk and film SZY membrane exposed to \(p\)O\({}_{2}\)(air)/\(p\)O\({}_{2}\)(H\({}_{2}\)) and \(p\)O\({}_{2}\)(air)/\(p\)O\({}_{2}\)(Ar) gradients. A high ionic transport number close to unity is the key requirement for the solid electrolyte to minimize a leakage current in a SOFC [41]. So, the Ni-cermet-supported 2.5 \(\upmu\)m-thick SZY membrane operating in the fuel cell mode (the air/hydrogen concentration cell) possesses almost pure ionic conduction in the temperature range of 500-600 \({}^{\circ}\)C, and therefore can operate as a SOFC membrane.
## 3 Diagnostic Reasoning for the EPS
### An ANFIS Monitor for the EPS  
To detect and isolate faults in the EPS, an ANFIS function is developed using the Neuro-Fuzzy designer toolbox in MATLAB R2019a [31; 32]. Using all the monitored parameters to develop ANFIS is impractical, as it consumes a lot of memory and ends up developing redundant rules. For example, from Figure 1, it is seen that power can be calculated and monitored from 11 nodes (one node from the generator, three nodes distributing power, and power supplied to four DC nodes, two AC lamp nodes, and one AC pump node). Considering these 11 nodes, and AC motor rotor speed as the 12 th monitored parameter, if every parameter is defined with three membership functions, the number of rules developed to map the relationship between the membership functions will be:  
\[\text{No. of rules}=(12\times 3)^{2}=1296 \tag{8}\]  
In reality, the EPS interacts with many aircraft systems through the LRUs. Monitoring power distributed at all these nodes will not be efficient for the diagnostic process as it will consume a lot of memory and time, and the simulation might not even converge in certain scenarios. Hence instead of using all monitored parameters, only a few should be selected for further analysis to detect the presence of a fault in the EPS. In this paper, the Power_Generated parameter is chosen for the purpose of diagnosis. This is due to the observation made from Table 1, where the total power generated in row 1 is almost equal to the total power consumed in rows 2-4, the slight difference accounting for the losses and the noise measurements. This eliminates the need to monitor both the power distributing and consuming nodes. Any presence of fault could ideally be detected by monitoring only the power generated from the EPS. A Gaussian membership function with three categories (low, medium, high) is defined for the monitored parameter, Power_Generated, using the Neuro-Fuzzy designer toolbox. Hence, the number of rules generated in this case becomes three. The ANFIS function is implemented as a monitor that screens the parameter Power_Generated, for a defined window of time. This defined time period is programmed to be any number of windows according to the user's interest, thus making the ANFIS monitor customizable.
## 3 Results and Discussion
### Contamination Behavior
#### 3.1.2 Penetration Depth Variation According to Location and Repetitive Welding Trials  
For the reference parameter set, when shielding gas was not supplied, obvious differences in penetration depth could be observed along the location, as shown in the cross-sections of the W1 sample (Figure 7a). Although a new coupling glass without contamination was installed before the W1 trial, the glass became contaminated over the welding time, and the penetration depth continuously decreased. At the last measuring point, the measured penetration depth was 0.75 mm, which is only 68.8% of the maximum penetration depth of 1.09 mm.  
The contamination accumulated continuously with the weld trials, and a penetration depth of 0.17 mm was obtained for the W10 trial, which was only 15.6% of the penetration depth observed for the W1 sample (Figure 7b). The results showed a severe reduction in penetration depth during the  
Figure 6: The contamination of the coupling glass after the 10 th welding trial under the reference condition: (**a**) image of the coupling glass and (**b**) contamination index distribution within the laser transmission region.  
Figure 7: Penetration depth according to location: (**a**) 1 st welding trial (W1) samples and (**b**) 10 th welding trial (W10) samples.  
welding trial and between welding trials, which caused inconsistent welding quality that would not be acceptable in most applications. This confirmed that the contamination of the coupling glass needs to be controlled to maintain uniform weldability. In addition, the penetration depth from cross-section (a) to cross-section (e) of W10 is almost the same, indicating that the contamination of the coupling window does not increase indefinitely with time but seems to saturate to a certain value.
## 3 Results  
The results of the comparison between the FB and the DIBH treatment plans are shown in Table 2. Among the 36 patients, the DIBH treatment plans which were delivered to the patients, resulted in better dose coverage to the target, in terms of V95% to the PTV (Wilcoxon signed rank test \(p\) = 0.002), compared with the FB plans which were calculated for comparison. Interestingly, the maximum PTV dose showed a decrease with DIBH which was borderline significant (\(p\) = 0.051).  
At the same time, the average maximum dose to the LAD was reduced to 0.56 (95% CI: 0.28-0.92) Gy in the DIBH treatment. In FB, the dose to the LAD was 11.11 (0.49-18.90) Gy. The difference was statistically significant (\(p\) << 0.001), and the reduction in maximum dose to the LAD, \(\Delta\)D\({}_{\text{L}}\), was 0.55 (95% CI: 0.07-11.99) Gy. This was largely due to the DIBH technique increasing the distance of LAD from the target region, from 0.08 cm to 2.19 cm (\(p\) << 0.001). The maximum dose to the heart was also reduced (\(p\) << 0.001). These results confirm the reduction of dose to the cardiovascular structures with DIBH. The value of _med_(\(\Delta\)D\({}_{\text{L}}\)) used to split patients into eligible or not eligible for DIBH, was 0.46 Gy.  
Regarding dose metrics to the other main organs at risk, contralateral (right) breast and ipsilateral (left) lung, no significant differences were found between FB and DIBH, with [PERSON] signed rank test \(p\) of 0.31 and 0.62, for lung and breast maximum doses, respectively.  
Figure 5 shows the model accuracy achieved during training in both data subsamples. The accuracy, specificity, sensitivity, F1 score, and area under the receiving operating characteristic curve (AUC), were 71.4%, 66.7%, 80.1%, 72.4%, and 69.4%, respectively, in the test dataset, after 35 epochs of training.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Mean Value (95\% CI)**} & \multirow{2}{*}{**[PERSON] Signed**} \\ \cline{2-2} \cline{4-4}  & **FB** & **DIBH** & **Rank’s \(p\)** \\ \hline Number of fields & 2.6 (2–5.3) & 2.6 (2–4) & 0.65 \\ \hline PTV V95\% (\%) & 95.0 (90.3–97.7) & 96.5 (93.9–98.7) & 0.002 \\ \hline PTV Dmax (Gy) & 42.9 (41.6–45.5) & 42.6 (41.6–43.7) & 0.051 \\ \hline Distance of LAD to PTV & 0.08 (\(-\)1.62,1.25) & 2.19 (1.44,3.75) & \textless{}0.001 \\ in BEV (cm) & 11.11 (0.49–18.90) & 0.56 (0.28–0.92) & \textless{}0.001 \\ \hline Heart Dmax (Gy) & 19.66 (0.61–30.20) & 0.93 (0.39–17.00) & \textless{}0.001 \\ \hline Contralateral Breast & 0.41 (0.11–0.87) & 0.50 (0.12–16.59) & 0.62 \\ Dmax (Gy) & 0.44 (0.19–12.61) & 0.63 (0.33–12.26) & 0.31 \\ \hline \end{tabular}
\end{table}
Table 2: Results of comparison treatment plans in FB and DIBH modalities.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 7 Conclusions  
In this paper, we looked into mixing software and hardware developments within one framework, targeting benchmarking of GNSS algorithms, and accessing new comparison KPIs for energy efficiency assessment. We have explored the feasibility of porting parts of an GNSS-SDR developed in a high-level programming language into hardware while minimizing the need for re-development. Using an AMD Zynq device and the associated PYNQ flow, we provided a proof-of-concept architecture to perform benchmarking of GNSS algorithms on an FPGA board for a more realistic estimation of power/energy consumption. We first reviewed our SDR named SyDR, developed in Python, and how it can be used to obtain a first estimate of the time required by each stage of the GNSS processing chain. Consequently, we highlighted the need for more realistic power/energy estimations to improve its benchmarking capacity. In response, we presented our Hard SyDR concept that couples Python with algorithms running in an FPGA. We outlined our process of converting our algorithms in Python via C/C++ and HLS into functional hardware designs. Next, we presented the first results of our implementation, illustrating the challenges encountered. These results show a clear need for design optimization to implement a complete replica of a GNSS receiver inside the FPGA selected. Yet, the new KPIs made available by Hard SyDR can be exploited to refine our implementations further. Future work will focus on going deeper in the exploration of the HLS process and the implementation of AxC techniques to achieve significant reductions in energy consumption in next-generation GNSS algorithms.  
Conceptualization, A.G., H.J.D. and J.L.; methodology, A.G., H.J.D. and J.L.; software, A.G., H.J.D. and J.L.; validation, A.G., H.J.D. and J.L.; formal analysis, A.G., H.J.D. and J.L.; investigation, [PERSON], H.J.D. and J.L.; resources, [PERSON], H.J.D. and J.L.; data curation, [PERSON], H.J.D. and J.L.; writing--original draft preparation, A.G., H.J.D. and J.L.; writing--review and editing, [PERSON], H.J.D., J.L., E.S.Q.-O., A.O., E.S.L. and J.N.; visualization, A.G., H.J.D. and J.L.; supervision, E.S.Q.-O., A.O., E.S.L. and J.N.; project administration, A.G.; funding acquisition, E.S.Q.-O., A.O., E.S.L. and J.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Union's Horizon 2020 Research and Innovation Programme under the [PERSON] Sklodowska Curie grant agreement number 956090 (APROPOS: Approximate Computing for Power and Energy Optimisation, [[http://www.apropos-itn.eu/](http://www.apropos-itn.eu/)]([http://www.apropos-itn.eu/](http://www.apropos-itn.eu/)), accessed on 5 January 2024).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Finite Element Model of Soliosis Brace with Increased Utility Characteristics
###### Abstract  
Orthoses are of critical importance in the field of medical biomechanics, particularly in the correction of spinal deformities. The objective of the current research was to improve the utility characteristics of the scoliosis brace without compromising its corrective capabilities. The orthotic shell of the Boston brace was used as the basis for this investigation. The finite element method (FEM) was used to evaluate the distribution of corrective forces through the device. The flow of force lines within the orthotic shell was determined by mapping the paths of maximum principal stresses. Areas of the device that had a negligible effect on overall stiffness were identified and material from these areas was eliminated. Minor modifications were then made to the redesigned shell to maintain its corrective stiffness. As a result of these changes, the weight of the braces was reduced without compromising its corrective stiffness. When subjected to corrective forces, the shell's displacement patterns in the transverse plane showed minimal changes from the original model, confirming that its corrective capacity remained largely intact. This research presents an innovative methodology for orthotic design and demonstrates that structural optimization based on the mapping of maximum principal stress pathways can significantly improve device functionality. The approach outlined here holds promise for future advances in the design of various orthotic devices, thereby contributing to the advancement of the field.  
FEM; orthosis; principal stress vector; principal stress trajectories; force flow lines +
Footnote †: journal:
# Construction Progress and Aviation Flight Test of BDSBAS
## 2 Overview of BDSBAS
### BDSBAS SF Solution  
The BDSBAS SF service guarantees the integrity of user positioning by broadcasting the satellite orbit, clock and ionospheric grid corrections, as well as the user differential range error (UDRE) and covariance information [33,34], as shown in Figure 4. After the positioning solution and the protection level calculation, the service performance can be further evaluated and analyzed.
# Visuo-Haptic Mixed Reality Simulation Using Unbound Handheld Tools
## 4 Validation Experiments and Results
### Tool Tracking Experiments  
The most essential requirement of the proposed concept is that the tooltip and the haptic-tip engage precisely. The nominal accuracy of fusionTrack 250 is 90 \(\upmu\)m within 1.4 m [29], which coversour simulation workspace. The tool's velocity also affects accuracy [30]. The hexapod has 3 \(\upmu\)m resolution and 0.5 \(\upmu\)m repeatability. Therefore, position data from the hexapod was used as a reference in this experiment.  
The tool was attached firmly to the gripper at its tip, as shown in Figure 6. It was moved with 20 mm/s velocity to a distance of 40 mm along the principal axes of {\(W\)} and stopped for 5 s at 5, 10, and 20 mm locations. The procedure was repeated at 40 mm/s. The tracker sends measurement data to PC-S, where the server application processes the data and transfers it to PC-H. All communications are over the UDP network at 100 Hz, hence the errors are calculated in PC_H whenever new data is available from the tracker. Table 1 shows the absolute mean (MAE) and root mean square (RMSE) values of the relative end-to-end tracking error, which includes measurement error, numerical error due to processing, and UDP delay. According to the first two columns, static tracking error when the tool was stationary at 5, 10, 20, and 40 mm locations was less than 0.2 mm. Dynamic tracking error almost doubled from 20 mm/s to 40 mm/s, however, the compound error was less than 1 mm.
# Spatial Pattern Simulation of Antenna Base Station Positions Using Point Process Techniques
## 2 Materials and Methods
### Point Process Analysis
Based on the Poisson point process, the mean number of events per unit area at the point \(s\), defined as \(\lambda(s)\), is given by [15; 16; 17; 18] Equation (3):  
\[\lambda(s)=\lim_{ds\to 0}\biggl{\{}\frac{E(N(ds))}{|ds|}\biggr{\}} \tag{3}\]  
where \(d(s)\) is a small region around the point \(s\), \(E(. )\) is the expected value, and \(N(d(s))\) is the number of events in the small region, defined as the first-order property considering a study region. Then, the expected value over the region \(A\) is given by Equation (4):  
\[E[N(A)]=\lambda|A| \tag{4}\]  
where \(A\) is the area of the sub-region, and \(\lambda\) is the value of the intensity. When the analysis involves two study regions, in this case a second order property would be considered given by [15; 16; 17; 18] Equation (5):  
\[\gamma\bigl{(}s_{i},s_{j}\bigr{)}=\lim_{ds_{i},ds_{j}\to 0}\biggl{\{}\frac{E \bigl{(}N(ds_{i})N\bigl{(}ds_{j}\bigr{)}\bigr{)}}{|ds_{i}|\bigl{|}ds_{j}|} \biggr{\}} \tag{5}\]  
where, under the condition of independence between the two regions A and B, the reflection of the spatial dependence in the process taking place is given by Equation (6):  
\[\text{cov}[N(A),N(B)]=0\text{ if }\Lambda\cap\text{B}=\emptyset\text{)} \tag{6}\]  
A stationary process is defined when the intensity inside the region A is constant, so \(\lambda(s)=\lambda\) and \(\gamma(s_{i}s_{j})=\gamma(s_{i}-s_{j})=\gamma(d)\) (depending only on direction and distance). For the isotropy case, the second-order intensity depends only on the distance between \(s_{i}\) and \(s_{j}\). A brief presentation of the previous research involving point process analysis in antenna stations is given in Table 1[19; 20; 21; 22; 23].
# Improved Bio-Oil Quality from Pyrolysis of Pine Biomass in Presurized Hydrogen
## 3 Results and Discussion
### Elemental Analysis of Pyrolysis Oil  
The oxygen content in pyrolysis oil is an important index of the reaction pyrolysis effect. The elemental analysis of pyrolysis oil under different atmospheres is shown in The oxygen content of pyrolyzed bio-oil in the hydrogen atmosphere was 17.16%, which was much lower than that in the nitrogen atmosphere. The most direct effect of hydrogenation is the reduction in oxygen content in pyrolyzed bio-oil caused by a large amount of oxygen bridge (C-O-C) and hydroxyl (O-H) bonds present in biomass. Oxygen bridge bonds and hydroxyl groups were destroyed during pyrolysis in the hydrogen atmosphere, decreasing the oxygen content of pyrolyzed bio-oil. Moreover, pyrolysis in the hydrogen atmosphere is focused on the hydrodeoxygenation reaction. The hydrogen element content was 7.06%, which was 5.17% higher than that in a nitrogen atmosphere because, in a hydrogen atmosphere, hydrogen combines with pyrolysis steam, while some enter the liquid product to form more stable species. The carbon content is basically unchanged from 71.55% to 72.11%, and biomass carbon content is not easy to lose. The nitrogen content increased from 1.94% to 3.66%, which was higher than that in the nitrogen atmosphere, indicating that the nitrogen volatiles in biomass samples are likely to combine with hydrogen to form stable nitrides (amines) in the hydrogen atmosphere. The subsequent GC-MS results also confirmed the formation of amines.