esa-sceva/satcom-chunk-collection huggingface dataset:
# Feedback Devices for Cardiopulmonary Resuscitation: A Narrative Review
## 4 Equipment Support of CPR Feedback Physiological Parameter Monitoring
### End-Tidal Carbon Dioxide Monitoring Equipment  
The infrared method is routinely utilized in clinical settings, primarily due to its main absorption wavelength for CO2 at 4260 nm. Based on this property, CO2 concentration can be deduced from its absorption rate. Two modes of application are prevalent for the infrared method: bypass flow and mainstream flow [45].  
In mainstream flow mode, the sensor is directly connected to the patient's tracheal tube connector, becoming an integral part of the artificial airway. The airflow actively traverses the sensor. The key advantages of the mainstream infrared method lie in its swift medical diagnostic response due to the sensor's direct contact with the airflow. Conversely, the bypass flow mode does not directly connect the sensor to the ventilation loop. Instead, exhaled gas is extracted through an air pump and then measured in a dedicated chamber. The reaction rate of recognition is moderately slower in this setup, leading to an approximately 3 s time delay between chest undulations and exhaled gas [45].  
Infinium Medical has developed the Cleo EtCO\({}_{2}\) Monitor with Vitals, an intuitive device for non-invasively measuring patients' vital signs, including blood pressure, SpO\({}_{2}\), rapid temperature, and exhaled carbon dioxide. The device supports continuous monitoring over extended periods and is applicable in a variety of settings. Its uses encompass both intubated and non-intubated applications, on-site inspections, bedside monitoring, and more [46].  
Masimo's Emma Capnograph, a portable real-time carbon dioxide analyzer, provides continuous and clear visualizations of carbon dioxide measurements. The device can display EtCO\({}_{2}\) and respiratory rate measurements, along with real-time ETCO\({}_{2}\) waveforms within a timeframe of 15 s [47].
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 4 CubeSat Design
### Orbit Dynamics  
The orbit-dynamics discipline computes the Earth-to-body position vector of the satellite in the Earth-centered-inertial (ECI) frame. In the orbit equation, the \(J_{2}\), \(J_{3}\), and \(J_{4}\) coefficients capture the fact that the Earth is not a perfectly spherical and homogeneous mass and is expressed by Equation (7).  
\[\begin{split}\overset{\rightharpoonup}{r}=-\frac{\mu}{r}\overset{ \rightharpoonup}{r}-\frac{3\mu J_{2}R_{s}^{2}}{2r^{3}}\left[\left(1-\frac{5r _{s}^{2}}{r^{2}}\right)\overset{\rightharpoonup}{r}+2r_{s}\hat{z}\right]- \frac{5\mu J_{3}R_{s}^{3}}{2r^{2}}\left[\left(3r_{s}-\frac{7r_{s}^{3}}{r^{2}} \right)\overset{\rightharpoonup}{r}+\left(3r_{s}-\frac{3r_{s}^{2}}{5r_{s}} \right)r_{s}\hat{r}\right]+\\ \frac{15\mu J_{4}R_{s}^{4}}{8r^{3}}\left[\left(1-\frac{14r_{s}^{2} }{r^{2}}+\frac{21r_{s}^{4}}{r^{4}}\right)\overset{\rightharpoonup}{r}+ \left(4-\frac{28r_{s}^{2}}{3r^{2}}\right)r_{s}\hat{z}\right]\end{split} \tag{7}\]  
where \(\overset{\rightharpoonup}{r}\) is the position vector of the satellite in the ECI frame, \(\mu\) is Earth's gravitational parameter, \(R_{e}\) is Earth's radius and \(J_{2}\), \(J_{3}\), and \(J_{4}\) are orbit perturbation coefficients. The \(J_{2}\), \(J_{3}\), and \(J_{4}\) terms must be considered because their effect is to rotate the orbit plane, which affects the power generation and communication discipline.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 4 Experiment and Results
### Processing Time vs. Performance  
Compare to the traditional fingerprint positioning, D2D collaborative positioning takes much longer processing time. The additional time is spent to communicate with other devices, acquiring positioning information for own and other devices, and processing delay. The more cooperation, the more processing time. Our laboratory computer was running on windows 8.1 operating system with Intel core i-5 processor having 2.30 GHz clock speed and 8 GB RAM. Positioning program was coded in Matlab 8.4 software. Figure 12 shows the relation between performance and processing time.  
Figure 11: Effect of the increase on the amount of measurements in the proposed method shown as the CDF of the positioning errors.  
Figure 12: Processing time vs. performance (Instead of meter, positioning error has been expressed in centimeter unit to make the figure more sensible where two scale -time and performance- come closer to each other).  
According to the traditional fingerprint method the recorded mean processing time was 103 millisecond (ms) per test fingerprint, that resulted in 8.68 m mean positioning error. On the contrary, when the proposed method was applied, positioning error decreased but processing time raised over 500 ms per test fingerprint. From Figure 12 it can be seen that processing time raised around 520 ms but positioning error decreases to 559 cm (or 5.59 m) when UE starts to communicate with other devices at least 3 times. Ascending trend of processing time and descending trend of positioning error can be seen in Figure 12. A decision regarding optimal resource management can be made based on this type of plotting.
## 4 Results and Discussion
### Simulation and Experimental Results Analysis
#### 4.3.3 Analysis of Hot Water Output Rates  
During the experiment, the flow rate was maintained at 441.18 L/h, which corresponded to a velocity of 0.29 m/s. The initial temperature difference was set at 18.66 \({}^{\circ}\)C, and it took 1470 seconds for the temperature to drop to 10% of its initial value. Notably, the tank achieved a hot water output rate of 88.42%. The temperature difference between the inlet and outlet showed an initial increase, followed by a decrease during the exothermic period. During the simulation, the simulation results show that the initial temperature difference of the elbow-type inlet is 19.1 \({}^{\circ}\)C, and it takes 1412 s to drop to 10%. The hot water output rate of the tank was 84.9%. Comparatively, the water distributor-type inlet had an initial temperature difference of 16.28 \({}^{\circ}\)C. The time to reach a 10% temperature drop was 1272 s, and the tank's hot water output rate was 76.39%. The hot water output rate of the elbow-type inlet structure was improved by 8.51% compared to the water distributor-type inlet structure.  
In a previous study [42], a hot water output rate of 74.98% was reported at an inlet flow rate of 253.82 L/h. Although they are consistent with our simulation results, it is evident that the elbow-type inlet and outlet configurations exhibit higher hot water output rates than the water distributor type. This enhancement suggests improved efficiency in the energy utilization of the system.  
Figure 15: Comparison of simulated Richardson number for different inlets.
## 5 Experiments and Results
### Determination of the Weights for Each Disturbance  
The weights (\(\alpha\), \(\beta\)) for each of the disturbances in (8) were first determined so that the average PESQ was maximized. This provided the necessary information for calculating the perceptual distance in the future experiments. The average PESQs according to the weight for the asymmetrical disturbance, \(\beta\) in (8) are plotted in Figure 5 where the weight for the symmetrical disturbance \(\alpha\) is given by \(1-\beta\). The correlations between the two variables (average PESQ and the asymmetrical disturbance) were \(-0.9321\), \(-0.9722\), \(-0.9888\), and \(0.7172\) for conversion pairs r2b (rms \(\rightarrow\) bdl), b2s (bdl \(\rightarrow\) slt), s2c (slt \(\rightarrow\) clb), and c2r (clb \(\rightarrow\) rms), respectively. This means that except for c2r, lowering the asymmetrical disturbances was helpful in increasing the PESQ. Such results are somewhat different from in the case of speech enhancement, where the asymmetrical disturbance contributed to an increase in perceptual quality [25]. This results indicated that the perceptual similarity between the converted speech and the target speech was not remarkably affected by the residual components of the reference speech spectra and the loss of the target speech spectra. Whereas in speech enhancement [25], the residuals of the unwanted components (noise spectra) and the loss of the desired components (signal spectra) highly affected the perceptual similarity to the original speech signals. A possible reason for this results is that in speech enhancement, the unwanted components are always less correlated with the desired signal components, and hence, the residuals of the unwanted components and the loss of the desired components seriously degraded the quality of the reproduced speech signals. Whereas in VC, both the unwanted components and the desired components correspond to the reference and target spectra, respectively. The degree of the correlation between the two components may be varied according to the underlying two speech signals (reference and target). In other words, usefulness of the asymmetrical disturbance may be determined by combination of the reference/target signals. For example, the two speech signals are perceptually more correlated for the pairs (rms, bdl), (bdl, slt), and (slt, clb), compare with the pair (clb, rms). The conversion of clb \(\rightarrow\) rms is female-to-male conversion, and hence, it can be reasonably assumed that the perceptual correlation between them is not as high as other pairs. The conversion of rms \(\rightarrow\) slt is also different gender conversion. However, the degree of the correlation in the perceptual domain between them is assumed to be higher than the pair (clb, rms).  
In the follows, the weights for each of the disturbances that yielded the highest average PESQ were adopted for each conversion pair.
# Hardware-in-Loop Modules for Testing Automated Ventilator Controllers
## 3 Results
### OxyVent Testing  
After the conversion of DO to SpO\({}_{2}\) values was implemented, the OxyVent module was tested with a closed-loop controller using the oxygen management logic described earlier (Figure 4). Three replicate runs at two sampling rates--5 s and 60 s--were averaged across each run and the resulting data are shown in Figure 11. The controller configuration in Figure 11A updated every 5 s and reached the target SpO\({}_{2}\) range by keeping SiO\({}_{2}\) constant at 100% while adjusting PEEP to increase SpO\({}_{2}\). The controller in Figure 11B updated every 60 s and reached the target SpO\({}_{2}\) range by keeping PEEP constant at 5 cm H\({}_{2}\)O while adjusting SiO\({}_{2}\) from 100% to a final target of 90% due to overshooting the target SpO\({}_{2}\). Overall, both controller configurations successfully adjusted either SiO\({}_{2}\) or PEEP to reach the target SpO\({}_{2}\) programmed in the controller logic.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Partial Air/Partial N\({}_{2}\)**} & \multicolumn{3}{c}{**Partial Air/N\({}_{2}\) Off**} & \multicolumn{3}{c}{**Full Air/Full N\({}_{2}\)**} \\ \hline  & & 0 psi & 5 psi & 10 psi & 0 psi & 5 psi & 10 psi & 0 psi & 5 psi & 10 psi \\ \hline \multirow{2}{*}{DO} & Min & 2.43 & 5.57 & 6.38 & 2.55 & 6.72 & 7.98 & 2.39 & 7.22 & 7.97 \\  & Max & 5.61 & 6.42 & 7.26 & 6.72 & 7.98 & 9.72 & 7.24 & 7.98 & 9.34 \\ \hline \multirow{2}{*}{Std Dev.} & Min & 0.271 & 0.205 & 0.302 & 0.246 & 0.282 & 0.372 & 0.0873 & 0.0451 & 0.0404 \\  & Max & 0.650 & 0.360 & 1.03 & 0.493 & 0.426 & 0.604 & 0.193 & 0.154 & 0.344 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of minimum and maximum values of DO and the respective minimum and maximum standard deviation values at the varying pressure steps of 0, 5, and 10 psi.  
Figure 10: **Characterization of the OxyVent module PEEP functionality**. Average results for three air–nitrogen ratios (n = 3 each) and their dissolved oxygen (DO, ppm) values across three different pressure steps (P\({}_{\text{can}}\)) held for five minutes. (**A**) DO values over time without normalization and (**B**) with normalization to their minimum values; shaded regions denote the standard deviation for each air–nitrogen ratio and the three pressure steps labeled above the horizontal axis on each plot.
## 2 Materials and Methods
### Testing Procedures
#### 2.3.2 Dynamic Balance  
Dynamic balance was assessed with the Lower Quarter YBT kit (Functional Movement Systems, Lynchburg, VA, USA) in three directions (anterior, posteromedial, and posterolateral) for both limbs. The procedure was performed according to previously described and standardized protocols [27; 28; 29]. To standardize the results, lower limb length was measured in the supine position from the anterior superior iliac spine to the medial malleolus maintaining pelvic alignment. YBT reach distances were then normalized to limb length and reported as a percentage (%LL) using the formula (reach distance/limb length) \(\times\) 1. Previous research has confirmed that the YBT demonstrates strong reliability, with ICCs between 0.75 and 0.91 across repeated trials [30; 31]. The standard error of measurement (SEM) ranged from 1.77% to 5.81%. Depending on the participant's age group and the direction of reach, the minimal detectable change (MDC) necessary to identify a clinically meaningful difference across sessions ranged from 4.90% to 16.10% [30].
# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 2 Related Work
In anticipation of 6G networks, this research introduces an innovative technique that employs geolocated user traces to advance traditional REMs into Network State Synthetic Images (NSIs), converting various cellular network operating states into comprehensive heat maps. While synthetic image generation for REMs has been previously considered [16], the application of deep learning (DL) techniques to process geolocated traces into an image-like format for 6G networks remains unexplored. Past attempts to utilize image classifiers for network fault detection were hindered by reliance on inadequately processed images. This research, conversely, advocates the creation of NSIs using image classifiers for network management, representing a significant advancement in the field.
## 5 Results  
### Spatial Analysis of the Calibration Site  
As spatial homogeneity is one of the most important factors for the assessment of calibration sites [70; 72; 83; 84; 85], the calculation of [PERSON]'s \(I\), the Getis Ord \(Gi\), CV and TOA reflectance were essential for the selection of specific areas in the Frenchman Flat site, for the calibration (Figures 4 and 5).  
Particularly, \(I\) and \(Gi\) provided valuable information regarding the spatial patterns that are undetectable to CV or to global indicators of spatial dependence [83; 84; 85]. It must be mentioned that, as a result of the applied thresholding criteria, about 85% of the samples used for the estimation of FASat-C gains presented a CV lower than 1%. This has ensured that the most homogeneous and spatially-autocorrelated areas of Frenchman Flat were used for the cross-calibration of FASat-C (Figure 6). This is in line with the research of [PERSON] et al. [56] and [PERSON] et al. [58], who selected regions of interest located in areas with such relative variability for Cal/Val activities of the Chinese High Resolution Imaging Satellite GaoFen-1.  
After excluding outliers, the following number of homogeneous samples were used for the calibration: 127 for the blue (B1), 125 for the green (B2), 128 for the red (B3) and 129 for the NIR (B4) bands, respectively. Since the amount of photons reaching the sensor and their energy vary according to wavelength, each band was considered as an independent experiment. The FASat-C adjusted DNs and the RapidEye radiances exhibited a linear behavior and a high degree of correlation.  
Figure 4: (**a**) Moran’s _I_: the areas in white represent high spatial autocorrelation; (**b**) Getis Ord _Gi_: the areas in red correspond to clusters of high reflectance. The highest values of \(I\) and _Gi_ are concentrated at calibration areas with TOA reflectance higher than 0.43.  
Figure 5: (**a**) TOA reflectance: the areas in red present reflectance values higher than 0.48; (**b**) CV: the areas in black present CV lower than 2%.
# Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
## 3 Experiment and Result Analysis
### Data Preparation  
In this paper, we selected the power consumption data of a copper smelting enterprise in Yunnan Province from 1 June 2021 to 25 June 2021, including 7200 data as the training set, and finally we selected 6810 data with an abnormal score lower than 0.6 to train the autoencoder. A total of 620 data from 24 June 2021 to 25 June 2021 were selected as the validation set and 4900 data from 26 June 2021 to 10 July 2021 were selected as the test set.
## 5 Summary and Discussion
Proper calibration, which includes quality indicators related to the uncertainty of the measurements, and correct installation (e.g., thermal stabilization of the sensor; fixed fiber optic setup; installations to keep birds, insects, and dirt away from the reference measurement), is the cornerstones of reliable measurements. Still, constant changes in atmospheric conditions, in combination with the fast response of F to E changes and environmental stress, make it necessary to track the measurement quality and eventually omit uncertain measurements. The related suggestions discussed in Sections 4.3 and 4.4 are considered as a starting point, but more data-driven research is needed in this direction. Similarly, it is crucial to report metadata and ancillary data, particularly for the measurement of F since it is sensitive to many confounding factors. It is expected that the ongoing research on F will reveal which additional information is necessary. It is advisable to record as many of the parameters stated in Section 4.4 as possible to improve the comparability of measurements among experiments and scales. All of these efforts will complement traditional field spectroscopy in its role, as defined by [PERSON] [1] 30 years ago: \"Firstly, it acts as a bridge between laboratory measurements of spectral reflectance and the field situation and is thus useful in the calibration of airborne and satellite sensors. Secondly, it is useful in predicting the optimum spectral bands, viewing configuration and time to perform a particular remote sensing task. Thirdly, it provides a tool for the development, refinement, and testing of models relating biophysical attributes to remotely-sensed data\" [1]. A crucial step is to investigate how measurements on different scales can complement each other and be combined to gain ecosystem-wide insights. Since laboratory and field scaling experiments are laborious and possibly insufficient to address the complexity and dynamics of the underlying processes determining variations in F, the refinement and use of (3D) radiative transfer models will play a crucial role in facilitating such scaling research.
# Analysis and Synthesis of Architectures for Automotive Battery Management Systems
### E/E- Architectures
Ethernet networks can handle data more than 10 Mbps [23], which makes it ideal for centralized E/E-architectures and zonal gateways, as well as data intensive domains such as ADAS. A so-called Ethernet backbone handles the communication between the different gateways and domains [23].
## 3 Results and Discussion
### Damping Ratio
Afterwards, the damping ratio can be calculated out. Figure 11 shows the variation of damping ratios versus axial total strains under different confining pressures, cement contents for different soil types. The damping ratio increases slowly with the increasing axial total strain, but its value ranges from 0.07 to 0.08. However, the damping ratios of the lightweight soil obtained by [PERSON] et al. [15] have a larger variation range from 0.05 to 0.20 when the strain increases from 1% to 10% (see Figure 11d). This is mainly due to the different test methods and the corresponding formation of tested lightweight samples, with lower cement content and higher volume occupancy of EPS beads. Although the difference of damping ratios are relatively small under different conditions in this study, there are still some characteristics that can be detected. For example, the sandy lightweight soil has lower damping ratio than the silty lightweight soil, and larger confining pressure and higher cement content obtain smaller damping ratios. There are two kinds of damping in soils, one is the dissipation damping, and the other is the material damping. The former is caused by the diffusing of energy that is accumulated in the soil to the outside world in forms of surface wave and body wave, while the latter is generated from the friction between particles and the viscosity of pore water and air. However, this study is not focusing on the dynamic problem under the action of high frequency. The wave is not considered and the dissipation damping can be assumed to be zero, then the damping can be deduced to be a completely material damping. Actually, the bonding strength of the specimen is relatively strong due to the adopted cement contents, so there is little difference between the dislocation and slip of particles under the confining pressures and the strain levels in this study. Figure 10: Simplified form of repeated loading and the stress-strain relationship. Although little differences in hydration products, pore water, and pore gas have been detected, there are still some other points worth analyzing. The larger the strain is, the greater the damage of the specimen is. The greater the dislocation and slip between particles are, the greater the friction between particles is, and the greater the material damping ratio would be. With increasing confining pressure, the specimen is compressed denser, and then the particles' dislocation and slip are decreased with smaller friction, as well as the smaller damping ratio. The same situation is still applicable to the cement content. From the previous analyses, more cementing substances are produced in the sandy lightweight soil than in the silty clay lightweight soil for a certain curing time. Therefore, the interaction between grains of sandy lightweight soil is closer and the filling degree of the void is larger. Moreover, the dislocation, slip, and friction between grains are reduced, indicating that the discharge of pore water and pore gas in the lightweight soil is increased, and then a smaller material damping ratio is obtained.