esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Reliability  
Commonly, reliability can be tested determining the within-subject variation, changes in the mean and retest correlation [26]. Reliability of the measures are critical for LPS systems, mainly to ensure the consistency and allow comparisons over time and in a repeated way (ref). The most common tests to be applied in reliability analysis are the coefficient of variation or typical error of measurement (TEM) and, in some cases, the intraclass correlation test (ICC) [27]. Following the suggestions of [27], reliability can be interpreted as good for variability lower than 5%, moderate between 5 and 10% and poor for 10% or above.  
From the included studies of this systematic review, four of them [17; 24; 30; 31] proposed to test the reliability levels of LPS systems. A 20-Hz LPS system (Kinexon One) using 12 antennae was tested by [PERSON] et al. [31], revealing typical errors between 0.1 (criterion variable of 10m jogging with jump) and 1.7 (criterion variable of 129.6 m entire circuit). The LPS revealed good reliability for the entire distance covered, walking over 10 m with change of direction (COD), sprinting with CODs, sprinting over 30-m, sprinting over 5-20 m and theoretical maximal force and horizontal power [31]. However, in comparison to the GPS tested (10-Hz and 18-Hz), the LPS revealed greater noise at distances covered during standing, mainly caused by a shift in the zero-velocity line and increase in the velocity due to performed turning maneuvers [31]. Despite that, comparisons of reliability between the GPS and LPS was mainly favorable to LPS [31]. An UWB from RealTrack Systems was tested for its intra- and interunit reliability [17]. The intraunit reliability of UWB in mean velocity varied between 0.895 and 0.999 of ICC (95% of confidence interval) and the low and upper (for interunit variability) ranged between \(-\)0.09 and 0.42%. In the case of distance covered, the typical error of UWB varied between 0.94 and 4.87% and the lower and upper bias was between \(-\)2.65 and 2.06%. Thus, it was concluded that the UWB was reliable for distance covered and mean velocity [17]. Another study testing interunit reliability of UWB of the RealTrack system presented ICC values of 0.65 and 0.88 for x- and y-axis, respectively [17].  
The NBN23 LPM system (Nothing But Net) was tested for its reliability. The coefficient of variations for walking, running and sprinting was 1.1-3.0%, 0.9-4.1% and 0.6-4.3%, respectively [30]. Comparisons between the LPM system and the taped measurement were also conducted, showing that the differences between the trials only varied for the 0-15 m at walking speed and interparticipantdifferences were found at 0-10 m in walking. Thus, results of the study showed that the NBN23 was reliable for monitoring distance and running time.  
Summarizing the evidence regarding the reliability of LPS systems, it is possible to conclude that the three systems (Kinexon One, RealTrack Systems and NBN23) had coefficient of variations below 5% thus revealing reliability for measuring distances covered at different speeds and also for quantifying velocities achieved during the tasks.
## 4 Discussion
Additionally, a 30-point reduction in disease severity on the WOMAC scale was observed; such a scale includes parameters relative to pain, stiffness, and physical function related to the ability to execute activities of daily living. Our results additionally show a statistically significant three-point increase in quality of life as self-reported via SF-12. Lastly, we observed a reduction in patients' analgesic intake, a particularly crucial endpoint when considering the toxicity burden borne by traditional pharmacological treatment options for knee OA, namely, NSAIDs. Such results, therefore, greatly support the role of hybrid HA viscosupplementation as a nonpharmacological approach to relieve pain, reduce disability, improve quality of life, and limit the risk of polypharmacy in overweight patients. Individuals affected by OA suffer from more comorbidities and lead a more sedentary lifestyle as compared to healthy peers, and studies have observed a 20% higher age-adjusted mortality in such patients [36]. After viscosupplementation with hybrid HA, we observed a 54.9% improvement in patients' cardiorespiratory capacity as evaluated by 6 MWT, supporting a role for such treatment in improving outcomes in overweight and obese patients suffering from knee OA. Although a non-statistically significant increase in lean mass and a decrease in fat mass were observed post-treatment, significant variations were not to be expected after such a short follow-up period and after only one injection. Furthermore, exercise therapy is a fundamental adjunct in knee OA treatment, and the level of physical activity practiced by patients was not recorded as a therapeutic variable in this study. The authors, however, posit that, by combining a patient-oriented number of sittings with exercise therapy and prolonging observation over a longer follow-up period, the promising results in terms of pain reduction and disease severity will permit improvements in physical function in such overweight patients and, long term, an improvement in lean-to-fat mass percentage ratios. Limitations of the study include the small number of patients, which is distinctive of small single-center clinical trials, and the absence of a placebo or control group. The latter issue has been addressed in multiple studies and meta-analyses comparing HA with placebo (normal saline) injective treatments, with data proving that HA is superior to saline for the amelioration of symptoms associated with OA, albeit with sometimes contrasting evidence [37; 38; 39]. Lastly, a further limitation is the lack of long-term follow-up due to the short observation period of the study. Further studies are, therefore, warranted to assess the superiority of such a treatment in the knee OA population. It may also be useful to compare the effectiveness of repeated cycles of Sinovial H-L hybrid HA against a single cycle of treatment in this category of patients.
## 3 Results and Discussion
### Hydrogen Sorption Properties  
Volumetric measurements were carried out in a wide range of pressures and temperatures to evaluate the hydrogen sorption capacity of the TiZrVNbTa alloy. Since the original Pd-free fibers do not react with hydrogen at room temperature without preliminary activation, they were subjected to heating in a vacuum (1 Pa) for 2 h at 673 K. Then, the reactor was filled with hydrogen at a pressure of 40 bar at the same temperature. The maximum value of the absorbed hydrogen reached 1.6 wt.% (H/M = 1.5). It is worth noting that, under the conditions described above, we were not able to measure the absorption isotherm step by step. Moreover, the subsequent hydrogen desorption was not complete: the re-hydrogenation, although it proceeds at room temperature, shows a noticeably smaller capacity of about 1.25 H/M. Only a separate long-term degassing at a temperature of 673 K for 10 h allowed us to remove the maximum possible amount of hydrogen and to characterize the products of such dehydrogenation. The XRD analysis of the hydrogenation products (Table 2) indicates that the main phase is the fcc hydride (space group Fm\(\overline{3}\)m). The transformation of bcc to fcc is complete; no additional phases were found. The volume expansion after hydride formation was 30.2%, which is 18% per 1 H atom. This value is slightly higher compared to literature data for related TiVZrNbTa alloys (14%) [47]. After prolonged dehydrogenation, the transition from the fcc to the original bcc structure has been recorded. Compared to the initial alloy, the unit cell parameter increases after dehydrogenation, which corresponds to the formation of a solid solution phase with a volume expansion of 1.9%. Assuming a linear dependence of the volume expansion on the hydrogen concentration, the residual hydrogen content is about 0.1 H/M. The applied palladium coating dramatically increases the activity of the alloy in relation to hydrogen. Already, the first hydrogenation proceeds at room temperature without any additional activation and without a noticeable induction period. The recorded hydrogen absorption isotherm for Pd@TiZrVNbTa microfibers is shown in Figure 4. We note the absence of a clear plateau on the isotherm, which would allow us to distinguish individual phase transformations as the amount of absorbed hydrogen increases. The maximum hydrogen sorption capacity was 1.7 H/M (1.8 wt.%), which is slightly more than for palladium-free fibers. According to the XRD results (Figure 3d, Table 2), a single-phase product is formed with the same unit cell volume expansion as for the original alloy (30.2%). Comparing the resulting isotherm with an isotherm of the second cycle for the palladium-free alloy (Figure 4), there is a significant increase in absorption pressure. This phenomenon is often observed in metal hydride systems, and it may be associated
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 2 Literature Review
### Possible Moderator Variables
#### 2.3.1 Research Context  
_Sample size._ Sample size is an important moderator variable. Researchers have concurred that an increase in class size leads to a reduction in teacher-student interaction and a subsequent decline in student satisfaction with online courses, which has a negative impact on student achievement [50; 51]. Small classes, typically comprising 20-25 students, have been shown to produce more positive academic outcomes compared with larger classes [52; 53]. [PERSON] and [PERSON] [54] reported that students were most satisfied with online courses when the class size was limited to 23 students. Based on this evidence, the present study adopted 23 as the threshold for defining a small class. For studies with a control group, a total sample size of 46 or fewer was considered small; for studies without a control group, any sample size above 23 was considered regular-sized.  
Discipline.Discipline is a crucial moderator variable that has an impact on the effectiveness of technology-enhanced learning [55]. VR-related meta-analyses have been conducted in various disciplines, including science, mathematics, medicine, and other subjects [13; 37]. Disciplines vary in terms of knowledge content, structure, and presentation, so when technology is applied to online learning, it may produce varying learning effects in different disciplines. VR may be more appropriate for some disciplines, while VC platforms may be more suitable for others. To investigate the reasons for the differences in the learning effects, this study considered discipline as a moderator variable and categorized it into four groups: natural science, humanities and social sciences, medical science, and engineering and technology sciences.  
Grade level.Grade level can affect learning effectiveness [10; 56]. It is important to consider the cognitive structures and abilities of students in different grades [57]. Younger students may find it easier to adapt to new technologies such as VR, while older students may be more disciplined and self-directed. This can affect their performance in technology-enhanced online learning environments. We therefore examined whether there were differences in learning outcomes between K-12 and college students to provide insights into choosing the appropriate online learning support technology.
## 6 Case Study
### Computational Result Analyses
The SIT and IS of \(\boldsymbol{\Psi}^{(41)}\) are the best at 0.1471 and 0.1200, respectively, and the TIT of \(\boldsymbol{\Psi}^{(42)}\) is the best at 0.1233. The TIT of most solutions is larger than that of the real timetable; this is because the planning period in this section is short, the fluctuation of passenger flow with time is not obvious (approximately linear in Figure 13), and the TIT in the real timetable is low. When managers expect to obtain a higher service level than the real timetable, they can pay attention to the Pareto optimal solution in region II, for a smaller cost, they can look in region IV, and for trade-off solutions, they can see region III. Figures 17-19 show the timetables of the representative Pareto optimal solutions \(\boldsymbol{\Psi}^{(1)}\), \(\boldsymbol{\Psi}^{(39)}\), and \(\boldsymbol{\Psi}^{(65)}\) of regions II, III, and IV, respectively. The single line denotes four vehicles per train, while the double and triple lines denote six and eight, respectively. The color of the line denotes the train loading rate, and the number next to the line denotes the train type and number. Figure 19: Trajectories of trains and passenger loading rate in solution \(\boldsymbol{\Psi}^{(65)}\). Figure 20: Comparison of Pareto fronts of line plan with different numbers of train type. Second, \(K=2\) has the highest cost with the same service level in region II and a similar performance to \(K=3\) in regions III and IV. \(K=3\) performs better when the service level is low (TTTP is greater than \(2.15\times 107\)), and \(K=4\) performs better when the service level is high. The sensitivity of the service level to the cost increases with an increasing \(K\), which is reflected in the slope of the Pareto front curve. For example, the service level is significantly reduced and the cost is reduced slightly if \(K=4\); that is, by increasing the lower cost, the service level can be significantly improved. In region III, \(K=3\) is better than \(K=2\), followed by \(K=4\) in general; in other words, a larger \(K\) is not necessarily better. The relationship between time and cost is approximately linear. The TTTP with \(K=3\) is sensitive when the cost difference is approximately 0. \(\mathbf{\Psi}^{(39)}\) with \(K=3\) has the optimal TTTP, whose in-vehicle time, congestion penalty, and fatigue penalty are close to \(\mathbf{\Psi}^{(27)}\) of \(K=2\), but with smaller waiting times. These two solutions have a higher TTTP optimization rate, owing to the better matching of the spatial imbalance of flow (i.e., low SIT and IS). \(\mathbf{\Psi}^{(53)}\) with \(K=3\) is the optimal cost difference, followed by \(K=4\). This is because the costs of vehicles and staff, which account for the largest proportion of the cost difference, are improved significantly. Finally, in region IV, \(K=2\) is better than \(K=3\), followed by \(K=4\) in general, while they all have lower costs and the curve difference is small.
###### Abstract  
Evaptranspiration (ET) is an important component of the Earth's energy and water cycle via the interaction between the atmosphere and the land surface. The reference evapotranspiration (ET\({}_{0}\)) is particularly important in the croplands because it is a convenient and reasonable method for calculating the actual evapotranspiration (AET) that represents the loss of water in the croplands through the soil evaporation and vegetation transpiration. To date, many efforts have been made to retrieve ET\({}_{0}\) on a spatially continuous grid. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) product is provided with a reasonable spatial resolution of 500 m and a temporal resolution of 8 days. However, the applicability to the local-scale variabilities due to complex and heterogeneous land surfaces in countries like South Korea is not sufficiently validated. Meanwhile, the AI approaches showed a useful functionality for the ET\({}_{0}\) retrieval on the local scale but have rarely demonstrated a substantial product for a spatially continuous grid. This paper presented a retrieval of the daily reference evapotranspiration (ET\({}_{0}\)) over a 500 m grid for croplands in South Korea using machine learning (ML) with satellite images and numerical weather prediction data. In a blind test for 2013-2019, the ML-based ET\({}_{0}\) model produced the accuracy statistics with a root mean square error of 1.038 mm/day and a correlation coefficient of 0.870. The results of the blind test were stable irrespective of location, year, and month. This outcome is presumably because the input data of the ML-based ET\({}_{0}\) model were suitably arranged spatially and temporally, and the optimization of the model was appropriate. We found that the relative humidity and land surface temperature were the most influential variables for the ML-based ET\({}_{0}\) model, but the variables with lower importance were also necessary to consider the nonlinearity between the variables. Using the daily ET\({}_{0}\) data produced over the 500 m grid, we conducted a case study to examine agrometeorological characteristics of the croplands in South Korea during the period when heatwave and drought events occurred. Through the experiments, the feasibility of the ML-based ET\({}_{0}\) retrieval was validated, especially for local agrometeorological applications in regions with heterogeneous land surfaces, such as South Korea.  
agrometeorology; evapotranspiration; cropland; machine learning 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (
# Phase Alignment of an Array Optical Telescope System Using Balanced Detection
## 3 Simulation Experiment Process
### Transmitter

Table 2: Parameters design of a transmitter. Step 2. The modulated data is transmitted to the NRZ pulse generator to output the NRZ signal with digital information. The output optical power of the laser is set to 10 dBm. The NRZ signal and the laser carrier signal are sent to the first stage LiNbO3 MZM to generate the NRZ-DPSK signal. The NRZ-DPSK signal and the sinusoidal voltage signal are input to the two-stage MZM, and the frequency of the driving voltage is set to be one half of the data bit rate. CSRZ-DPSK signal is obtained corresponding to the null offset point of the LiNbO3 MZ transfer function, when the carrier frequency is 0. Step 3. Observing the power spectrum and the waveform of the CSRZ-DPSK modulation signal. The energy of the CSRZ-DPSK modulated signal at the transmitting end was recorded as \(-21.857\) dBm.
# Late Reverberation Synthesis Using Filtered Velvet Noise +
## 2 Velvet Noise
### Velvet-Noise Convolution  
Time-domain convolution of a signal with velvet noise can be highly economical computationally. The samples of the velvet-noise sequence \(s(n)\) are used as FIR filter coefficients. Velvet-noise convolution (VNC) is very fast to compute, because all multiplications by zero can be dispensed as their locations in the sequence are known. Additionally, as the non-zero samples contained in the velvet noise are either \(-1\) or \(1\), multiplications are not needed. Thus, convolution with velvet noise reduces to a sparse multiplication-free convolution.  
Figure 1: (**a**) Non-zero sample values, (**b**) the autocorrelation function, and (**c**) the estimated spectrum of a velvet-noise sequence. In (**a**), the vertical dashed lines indicate the grid boundaries. In (**b**), the value of autocorrelation at zero lag is 1.0, but this first value is truncated in the figure.  
In practice, then, the input signal is propagated in the delay line of the filter, and only those input signal samples which coincide with the non-zero coefficients of the velvet-noise sequence are added together to produce the output. One idea is to separately run through the indices of coefficient values \(+1\) and \(-1\), add the corresponding sample values taken from the delay line, and subtract the two sums. This VNC process can be formulated as:  
\[x(n)*s(n)\ =\ \sum_{n_{+}}x[n-k(m_{+})]\ -\ \sum_{m_{-}}x[n-k(m_{-})]\, \tag{4}\]  
where \(x(n)\) is the input signal, \(*\) denotes the convolution, and \(k(m_{+})\) and \(k(m_{-})\) contain the indices of the positive and negative impulses, respectively, in the velvet-noise sequence \(s(n)\). This multi-tap delay-line implementation of VNC is illustrated in Figure 2.  
For example, when 5% of the velvet noise coefficients are non-zero (\(+1\) or \(-1\)) and the filter length is \(L\) samples, computing an output sample requires 0.05\(L\) additions and no multiplications. For a \(1-s\) noise sequence at the 44.1-kHz sample rate, the filter length is \(L\) = 44,100, and this yields 2205 additions per output sample. For comparison, a regular FIR filter of the same length requires \(L-1\) = 44,099 additions and \(L\) = 44,100 multiplications, or 88,199 operations, to compute each output sample, which is 40 times more than using VNC.
## 5 Conclusions  
This work successfully proposed a porous model to numerically investigate the fluid flow and heat transfer behaviors of the pin-fin heat-sink array situated in a rectangular channel in an in-line arrangement with various relative center-to-center longitudinal and transverse distances between heat  
Figure 17: Comparison results of Nusselt number with semi-empirical predictions and experimental data: (**a**) Sample 3 and (**b**) Sample 5.  
sinks. The present porous approach used the Brinkman-Forchheimer model for fluid flow and two-equation model for heat transfer. The pin-fin heat sinks with various porosities and pin-fin numbers were employed. Some major conclusions are summarized as follows:  
(1) By comparing with downstream heat sink, more air inflow leaks from the upstream heat sink. The heat sink with lower porosity or larger number of pin fins has less air inflow and more leakage flow. The air flow entering the heat sink is low and the leakage flow is high as the relative transverse distance (\(X_{T}^{*}\)) between heat sinks is large. However, the effect of the relative longitudinal distance (\(X_{L}^{*}\)) between heat sinks on the air flow entering the heat sink and the leakage flow is insignificant. Changing \(X_{L}^{*}\) did not affect the dimensionless pressure drop, but decreasing \(X_{T}^{*}\) would increase dimensionless pressure drop significantly. In addition, the array composed of heat sinks with lower porosity or larger number of pin fins has a larger dimensionless pressure drop.  
(2) The dimensionless solid temperature is higher than the dimensionless fluid temperature, meaning the solid and fluid have not yet reached local thermal equilibrium in the present numerical simulation. Therefore, the two-equation model used for this issue is necessary. The Nusselt number of various heat-sink arrays increases with decreasing \(X_{T}^{*}\), but does not vary with \(X_{L}^{*}\). When the porosity of the typical heat sink increases from 0.358 to 0.556, the average Nusselt number changes slightly. However, the average Nusselt number is increased significantly by 11.7%-24.8% when the porosity increases from 0.556 to 0.750, and then drops obviously when the porosity exceeds 0.750. Besides, increasing the number of pin fins continuously can increase the average Nusselt number. However, when the number of pin fins is large, the average Nusselt number increases with the number of pin fins slowly.  
(3) The present numerical simulations agree with the smoke flow visualization and heat transfer measurement of the typical experiment, proving the correctness of the present study. Finally, a semi-empirical correlation of Nusselt number for each heat sink in the present heat-sink array is proposed.