esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Models
### Calculation Model of Mesh Excitations for Helical Gears with Cumulative Pitch Deviations
#### 2.1.2 Determination of LPMS, STE, and CMS  
In our previous work, a basic LTCA model with high computational efficiency has been developed [26]. It can be used to calculate the short period mesh stiffness and transmission error of a cylindrical gear pair with short period tooth flank errors. In this section, considering the difference of tooth engagement process in different mesh cycles and the actual engagement condition of each tooth pair, the basic LTCA model is extended to an enhanced LTCA model, which can be applied to the calculation of long period mesh excitations.  
Figure 3 shows the enhanced LTCA model for a helical gear pair during one mesh cycle, and gives the arrangement of potential contact lines and contact points on plane of action (POA) \(B_{1}B_{2}B_{3}B_{4}\). For a helical gear pair with total contact ratio of 2 to 3, it can be divided into 2 teeth and 3 teeth zone. \(r_{p}\) and \(r_{g}\) are the radius of base circle of driving and driven gear. \(O_{p}\) and \(O_{g}\) are the rotational center of the two gears. \(\beta_{b}\) is helix angle of gear base circle. The rotational speeds of the two gears are denoted as \(\omega_{p}\) and \(\omega_{g}\). The Cartesian coordinate system \(X_{M}O_{M}Y_{M}\) is defined for LTCA calculation. Figure 4 shows the initial contact condition of one contact line pair before load for unmodified and modified gear pairs in consideration of cumulative pitch deviations. \(f_{\text{Pb}n1}\) and \(f_{\text{Pb}n2}\) are the pitch deviations for driving and driven gear respectively, \(M_{i1}\) and \(M_{i2}\) are the modification values for contact point \(i\) on driving and driven gear.  
Figure 2: End-side view and mating state of helical gear pair with cumulative pitch deviations.  
where \([\delta_{\text{G}}]\) denotes the summation of flexibility matrix of the mating gear teeth, \(\{F\}\) refers to the load distribution vector on contact points after load, \(\{\chi_{\text{C}}\}\) is the contact deformations of contact points, \(\{\omega\}\) is the residual clearances between contact point pairs after load, \(\{\xi\}\) is the initial clearances between contact point pairs before load. \(P\) is the normal force of gear pair.  
For each engagement position, the nonlinear matrix Equation (3) can be solved using an iteration contact algorithm, which contains two control loops [26]. When the solutions in each engagement position of all mesh cycles are solved one by one, the long-period load distribution and STE will be determined.  
The LPMS of helical gear pairs with cumulative pitch deviations can be determined using [15]  
\[k_{m}=\sum_{i=1}^{N}k_{i}=\sum_{i=1}^{N}\frac{F_{i}}{STE-\xi_{i}} \tag{4}\]  
The CMS of helical gear pairs can be calculated using [15]  
\[e_{m}=\frac{1}{k_{m}}\sum_{i=1}^{N}k_{i}\xi_{i} \tag{5}\]
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 2 Materials and Methods
### Detection of Land Cover Change  
The spatial patterns of land cover change are critical to understanding the LULC process and for helping policymakers adopt appropriate practices for the region's sustainable development. Thus, the gain and loss of major land cover types were calculated as follows (Equations (1)-(3)):  
\[\mathrm{K_{gain}=S_{b}-S_{a}} \tag{1}\]  
\[\mathrm{K_{0}=S_{b_{i}}=S_{a_{i}}} \tag{2}\]  
\[\mathrm{K_{loss}=S_{a}-K_{0}} \tag{3}\]  
where Sa and Sb are the land cover type at the initial and end of time in a given period, respectively. \(\mathrm{Sa_{i}}\) and \(\mathrm{Sb_{i}}\) are the unchanged area of the same land cover type in the initial and end of time.  
In addition, NDVI is useful for environmental monitoring, particularly in vegetation growth measurement (Equation (4)) [17; 19; 37]. Therefore, the maximum NDVI value composition was calculated from all the available images in the years of 1990, 2000, 2007, and 2019 to understand the influence of LULC changes in this study:  
\[\mathrm{NDVI=(NIR-Red)/(NIR+Red)} \tag{4}\]  
where NIR and Red are the values of near-infrared and red band, respectively, in Landsat image.
## 4 Results and Discussion
### WBVE and Lungs Capacity Impairment  
Lung commitment is found in individuals with COVID-19 and in the post COVID-19 survivors [34]. [PERSON] et al., 2015 [57] and [PERSON] et al., 2017 [86], investigated the effects of the WBV exercise in individuals with COPD. They used VP that generates mechanical vibration. However, each study used a different model of vibrating platform and vibration protocol. The number of sessions ranged from 12 to 24 sessions, the time of exposure to vibration ranged from 30 s to 60 s and the frequency used ranged from 24 to 30 Hz and 2 to 5 mm in amplitude. In relation to the total sample, it was composed of 85 individuals. Improvements in different clinical parameters were found in COPD individuals such as on the functional capacity, QoL, neuromuscular performance, cardiorespiratory responses, and muscle components related to exercise capacity [66; 74].  
[PERSON] et al., 2015 [57] pointed that the WBV may potentially be a safe and feasible way to improve functional capacity in the 6 MWT of patients with COPD undergoing a training program on the vibrating platform as well as in all domains of the St. George Respiratory Questionnaire. The protocol was performed in three sessions per week on alternate days with 4 weeks at low intensity and 8 weeks at high intensity. For the first 4 weeks, training was carried out for 10 min, with 30 s of low intensity training (2 mm) interspersed with 60 s of standing rest. For weeks 5-8, training was conducted for 15 min, and from weeks 9-12, training was conducted for 20 min, with 60 s of high intensity training (4 mm) interspersed with 30 s of standing rest in an anatomical position. [PERSON] et al., 2017 [86] verified the effect of four sets of two-minute duration performed three times a week on non-consecutive days. It was considered that the secret behind the positive effect of WBVT on exercise performance in patients with COPD may be related to improvements in neuromuscular performance, as opposed to muscular strength or central cardiovascular adaption. WBV training may be highly beneficial when incorporated into PR programs for COPD patients especially, with impaired balance performance and low exercise capacity. [PERSON] et al., 2019 [87], added that the WBV exercise, at different vibration frequencies, leads to an increase in oxygen consumption, heart rate, and oxygen saturation in subjects with COPD. Six series of 30 s with 60 s of rest interval between each series over four weeks was investigated. Furthermore, WBV exercise in COPD patients has been a safe exercise at all frequencies and types of squats, without causing dyspnea and fatigue (Table 7).
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 5 Conclusions  
In this study, an analysis based on the finite element method was performed to study the proposed communication method and evaluate communication characteristics of the system model when the environmental temperature change and the transmitting molecule volume were assumed. The results indicated that, even in the case of heterogeneous temperature distribution, communication characteristics were improved by more than 9.0 dB by estimating temperature using the maximum likelihood estimation method. Additionally, the SNR that realizes a \(BER\leq 10^{-3}\) was improved by approximately 11.1 dB by using the MLSE method under the assumption of actual volume of the transmitting molecule. A future study can focus on examining the communication characteristics under more realistic conditions such as continuous temperature variations with respect to time and location.  
Conceptualization, D.A.; methodology, J.K.; software, S.H.; validation, S.T.; formal analysis, S.T.; investigation, D.A. and G.F.; resources, D.A.; data curation, S.T.; writing--original draft preparation, S.T. and D.A.; writing--review and editing, J.K. and G.F.; visualization, S.H.; supervision, G.F. and D.A.; project administration, D.A.; funding acquisition, D.A. All authors have read and agreed to the published version of the manuscript.  
This research and development work was supported by the MIC/SCOPE #JP225006004.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Theoretical Background
### Radio Propagation Mechanism  
When radio signals travel, which are a form of electromagnetic waves, they interact with the media and objects they travel through. In the sequence of their interaction, the radio signals become weaker owing to refraction, reflection, diffraction, absorption and other propagation phenomena. The resultant effect of all the phenomena on propagated signals is signal propagation loss. The characteristics of the pathway or medium through which the radio signals travel determine the amount of propagation loss and the quality of the received signal that is attainable at the receiving terminal. Radio propagation loss is also governed by other sundry elements, particularly the transmitter power, receiver sensitivity and general antenna parameters such as antenna gain, antenna height and receiver location [1,2,37,38].  
The prominent factors that influence the number of signal path losses in a medium include diffraction, reflection, refraction, scattering and absorption, to mention a few. For example, diffraction arises when radio waves collide with huge obstacles compared to the propagating signal wavelengths. Moreover, diffraction occurs when radio signals bend around objects, especially those with sharp edges. This alteration often empowers the received radio signal energy to spread around the boundaries of the obstructing object [39; 40]. Diffraction is also influenced by the phase, amplitude, pathway and frequency of the transmitted waves.  
The environment in which the radio frequency signals travel (or are propagated) will undoubtedly negatively impact the signal. For example, radio wave signals and propagation loss vary extensively in correspondence to the terrain landscape, building structures and population density. Marshy, damp and sandy terrain also attenuate radio signals, primarily propagated low-frequency signals. In other words, signals travel faster over conducive terrain than in sandy and marshy or damp terrains.
# Shaking Table Tests of Suspended Structures Equipped with Viscous Dampers
###### Abstract  
In this study, a series of shaking table tests of a ten storey concrete suspended structure equipped with viscous dampers were carried out to evaluate the dynamic responses and vibration damping performance of suspended structures. The effects of link types between the primary structure and suspended floors and different seismic excitations on the response of suspended structure models was verified. The responses include the damping ratio, the frequency, maximum relative displacements, accelerations and maximum strains of the suspended structures. Test results showed that the damping ratio and the frequency of suspended structures installed with dampers (called damping suspended structure) are adjusted compared with a conventional suspended structure with rigid-bar links (conventional suspended structure). Maximum relative displacements of the primary structure of the damping suspended structure were distinctly smaller than those of the conventional suspended structure. However, the maximum relative displacement between the primary structure and the suspended floors of the damping suspended structure was significantly larger than that of the conventional structure, indicating that the swing of the suspended floor can help dissipate seismic energy. The peak acceleration and acceleration amplification factors of the damping suspended structure were less than the conventional suspended structure. Moreover, the peak acceleration response of the damping suspended structure was slightly behind the conventional suspended structure. The damping suspended structure certainly had a considerable and stable reduction for strain response, and the maximum strain response was decreased by 42.3%-72.7% for the damping suspended structure compared with the conventional suspended structure.  
damping suspended structure; viscous dampers; shaking table test; vibration damping effect; conventional suspended structure +
Footnote †: journal: Applied Physics Letters
# Estimation of Association between Healthcare System Efficiency and Policy Factors for Public Health
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DMU** & **Score (Bias-Corrected)** & **Bias** & **Score (Original)** & **SD** \\ \hline Luxembourg & 0.9249 & 0.0505 & 0.9754 & 0.0361 \\ Greece & 0.9142 & 0.0858 & 1.0000 & 0.0597 \\ Israel & 0.9140 & 0.0468 & 0.9608 & 0.0315 \\ Estonia & 0.9135 & 0.0865 & 1.0000 & 0.0712 \\ France & 0.9094 & 0.0706 & 0.9799 & 0.0700 \\ Portugal & 0.9093 & 0.0536 & 0.9629 & 0.0382 \\ Latvia & 0.8948 & 0.1052 & 1.0000 & 0.0769 \\ Ireland & 0.8853 & 0.0448 & 0.9301 & 0.0299 \\ Iceland & 0.8792 & 0.1208 & 1.0000 & 0.1135 \\ Poland & 0.8762 & 0.0606 & 0.9368 & 0.0439 \\ Sweden & 0.8586 & 0.1414 & 1.0000 & 0.1289 \\ Korea & 0.8577 & 0.1423 & 1.0000 & 0.1198 \\ Canada & 0.8545 & 0.0489 & 0.9034 & 0.0360 \\ Italy & 0.8469 & 0.0749 & 0.9218 & 0.0968 \\ Turkey & 0.8321 & 0.1679 & 1.0000 & 0.1600 \\ Mexico & 0.8101 & 0.1899 & 1.0000 & 0.2017 \\ Chile & 0.7970 & 0.2030 & 1.0000 & 0.2461 \\ Denmark & 0.7957 & 0.0353 & 0.8309 & 0.0239 \\ New Zealand & 0.7939 & 0.0418 & 0.8357 & 0.0315 \\ Spain & 0.7899 & 0.2101 & 1.0000 & 0.2748 \\ United Kingdom & 0.7892 & 0.0478 & 0.8370 & 0.0331 \\ Slovenia & 0.7863 & 0.2137 & 1.0000 & 0.2710 \\ Japan & 0.7824 & 0.2176 & 1.0000 & 0.2816 \\ Finland & 0.7786 & 0.0593 & 0.8380 & 0.0653 \\ Czech Republic & 0.7593 & 0.0616 & 0.8209 & 0.0657 \\ Norway & 0.7139 & 0.0378 & 0.7517 & 0.0347 \\ Hungary & 0.7099 & 0.0323 & 0.7423 & 0.0234 \\ Switzerland & 0.7029 & 0.0436 & 0.7465 & 0.0423 \\ Australia & 0.6973 & 0.0407 & 0.7380 & 0.0313 \\ United States & 0.6722 & 0.0499 & 0.7222 & 0.0415 \\ Belgium & 0.6189 & 0.0356 & 0.6545 & 0.0257 \\ The Netherlands & 0.5926 & 0.0315 & 0.6241 & 0.0221 \\ Slovak Republic & 0.5805 & 0.0398 & 0.6203 & 0.0375 \\ Austria & 0.4412 & 0.0313 & 0.4725 & 0.0226 \\ Germany & 0.4240 & 0.0348 & 0.4589 & 0.0280 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Efficiency scores (bias-corrected, original), bias, standard deviation (SD) of 35 OECD countries. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **NHS** & **NHI** & **SHI** \\ \hline N & 8 & 5 & 4 & 11 \\ Efficiency Mean & 0.8143 & 0.8156 & 0.6232 & 0.7740 \\ Efficiency SD & 0.0634 & 0.0738 & 0.2381 & 0.1318 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The types of healthcare system: number of countries, mean efficiency score, and standard deviation of efficiency score. Therefore, three factors that showed the lowest \(p\) values in order were selected as independent variables of the Tobit regression model presented as Equation (2) in Section 2.4. Table 5 shows the empirical results of the Tobit regression model. The three health care policy indicators of user choice of insurers (UCI) and user information (UI) were statistically significant at the 0.01 significance level and decentralization (DC) was statistically significant at the 0.05 significance level. In addition, all three health care policy indicators were found to have a negative impact on health care efficiency. Note that the policy indicator variables were converted into logarithmic scales.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 2 Materials and Methods
### LIVRD Algorithm  
The limiting inductor voltage ratio derivative algorithm is a voltage-measurement-based fault detection method [24]. It consists of measuring the voltage at both sides of a limiting inductor (Figure 1). During regular operation, both voltages are equal because the voltage difference through the inductor is zero. However, in the event of a fault, their values will diverge. The inductor voltage (Vinductor) is defined in (10).  
\[\text{V}_{\text{inductor}}=\text{L}\cdot(\text{di}/\text{dt})=\text{V}_{ \text{bus}}-\text{V}_{\text{link}} \tag{10}\]  
where \(\text{V}_{\text{bus}}\) is the voltage measurement at the bus side of the inductor, and \(\text{V}_{\text{link}}\) is the voltage measurement in the link side of the inductor, as shown in Figure 1.  
The LIVRD algorithm is divided into two secondary algorithms: VR and VRD. After taking the two voltage measurements, the VR is calculated, as in (11). During normal operation, the value of the VR is one.  
\[\text{VR}=\text{V}_{\text{link}}/\text{V}_{\text{bus}} \tag{11}\]  
Once the VR is calculated, its derivative, the VRD, is determined as shown in (12).  
\[\text{VRD}=\text{dVR}/\text{dt}=(\text{V}_{2}-\text{V}_{1})/(\text{t}_{2}- \text{t}_{1}) \tag{12}\]  
The VRD calculation consists of taking two consecutive samples of the already calculated VR and applying (11). In a steady state, the result for the VRD is zero.  
This algorithm is versatile and can be effective across various fault scenarios, including those with different fault resistances. However, it might be more complex to implement due to the need for precise inductor modeling and the integration of voltage measurements.  
Additionally, the algorithm offers directional capability, as it can distinguish between faults occurring in the forward and backward directions. Thus, it enables the discrimination of faults between buses and links. In the event of a link fault, the voltage Vlink decreases to a level below that of the Vbus. As a consequence, the calculated VR is less than 1, thereby causing the VRD to exhibit a negative value (13).  
\[\text{Link fault:}\ \text{V}_{\text{bus}}>\text{V}_{\text{link}};\ \text{VR}<1;\ \text{VRD}<0 \tag{13}\]  
Conversely, during a bus fault, the \(\text{V}_{\text{bus}}\) registers a lower value compared to the \(\text{V}_{\text{link}}\). Consequently, both the VR and VRD exhibit values greater than 1 and positive, respectively.  
\[\text{Bus fault:}\ \text{V}_{\text{bus}}<\text{V}_{\text{link}};\ \text{VR}>1;\ \text{VRD}>0 \tag{14}\]  
By comparing the computed VR and VRD against the predefined threshold values, as in (15), fault detection and discrimination can be accomplished. If both parameters exceed certain thresholds, a fault condition is identified. If not, a transient has been detected and the system continues normal operation while the algorithm restarts the calculation, as in [24].  
\[\text{Fault detection}=1\ \text{if}\ \text{VR}>\text{VR}_{\text{THR}}\ \text{and}\ \text{VRD}>\text{VRD}_{\text{THR}} \tag{15}\]
# Towards a Fast, Open-Path Laser Hygrometer for Airborne Eddy Covariance Measurements
###### Abstract  
Water vapor fluxes play a key role in the energy budget of the atmosphere, and better flux measurements are needed to improve our understanding of the formation of clouds and storms. Large-scale measurements of these fluxes are possible by employing the eddy correlation (EC) method from an aircraft. A hygrometer used for such measurements needs to deliver a temporal resolution of at least 10 Hz while reliably operating in the harsh conditions on the exterior of an aircraft. Here, we present a design concept for a calibration-free, first-principles, open-path dTDLAS hygrometer with a planar, circular and rotationally symmetric multipass cell with new, angled coupling optics. From our measurements, the uncertainty of the instrument is estimated to be below 4.5% (coverage factor k = 1). A static intercomparison between a dTDLAS prototype of the new optics setup and a traceable dew point mirror hygrometer was conducted and showed a systematic relative deviation of 2.6% with a maximal relative error of 2.2%. Combined with a precision of around 1 ppm H\({}_{2}\)O at tropospheric conditions, the newly designed setup fulfills the static precision and accuracy requirements of the proposed airborne EC hygrometer.  
water vapor; laser spectroscopy; near-infrared; dTDLAS; eddy covariance +
Footnote †: journal: Applied Sciences
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 4 Simulation Results and Analysis
Consequently, this leads to a reduction in the transmission delay of data link messages. In contrast, the collaborative channel perception [15] exhibits a certain deviation between the calculated channel occupancy statistics and the actual channel state due to unreasonable weight allocation. The local channel perception [5], however, is highly dependent on the traffic pulse statistical capability of a single node device. This can result in large fluctuations in channel occupancy statistics and lead to the inaccurate perception of channel states. Consequently, this causes errors in message transmission decisions, significantly increasing the message conflict probability and thereby increasing the message transmission delay. Therefore, it becomes difficult to meet the real-time requirements of data link message exchange. By analyzing the simulation results presented above, it becomes evident that the method proposed in this paper effectively mitigates the impact of fluctuations in node characteristic parameters on channel occupancy statistics. It can overcome the influence of the instability of factors such as node perception capability and electromagnetic environment on channel perception. Consequently, this leads to an improvement in the accuracy of channel state perception. Accurately perceiving channel state is pivotal to improving the reliability of data link message transmission decisions and reducing the probability of data link message conflicts. The method can provide technical support for enhancing the UAV cooperative channel perception capability. When considering identical channel occupancy statistics, the network throughput and transmission delay of our proposed method outperform both the local channel perception [5] and the collaborative channel perception [15]. Specifically, at a channel occupancy statistics value of 50%, the transmission delay of the method proposed in this paper is reduced by 21% in comparison to the local channel perception [5] and by 19% compared to the collaborative channel perception [15]. The method proposed in this paper also has certain limitations, which can be summarized into two main aspects. First, the broadcast messages used to share node characteristic parameters and pulse statistics will occupy a certain amount of spectrum resources. Second, when this method is used for large-scale networking, the complexity of node weight calculation will increase.