esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Use of a Shared System Interface:
## 4 Methodology
### Statistical Analyses  
In study 1, manipulating gaze convergence permitted to assess content validity of dyad GC index, that is, the extent to which our measure captures gaze convergence. To do so, we used Wilcoxon signed-rank test [55] with Statistical Analysis Software (SAS). This statistical method is particularly useful when the population is not assumed to be normally distributed and the sample size is small. Because our hypothesis was directional, we used one-tailed \(p\)-values.To get further insights related to patterns of task performance by dyads, we ran an ANOVA with a two-tailed \(p\)-value. Here we analyzed trial completion time to check for differences relative to the answer accuracy. This analysis would inform us whether dyads took different time to provide correct, wrong, or \"unknown\" answers. The significance level was set at \(\alpha=0.05\).  
To test hypotheses H1 and H2 through the change blindness experiment (Study 2), we used a linear regression with random intercept model. Hence, we tested negative association between dyad GC and dyad cognitive load measured using pupillary dilatation as mentioned earlier. We also tested negative association between dyad GC and dyad task performance. Moreover, we used one-tailed \(p\)-values and significance level of \(\alpha=0.05\) for the same reasons as in the first experiment. Finally, it is important to recognize that the images used in the Change Blindness task were of lower luminosity than the blank white screen displayed immediately prior to and after each image. There is a possibility that as the time taken to complete the trial increases, the eyes become accustomed to the less luminous images, causing pupil diameter to gradually increase, and thereby causing an artificial increase in cognitive load. To control for this, we added trial completion time as a control variable in the statistical model.  
Figure 5: Average gaze distances within dyads in each experimental condition. The sinusoidal trend in the Divergence condition is consistent with the fact that the blue and the red circles alternatively moved closer to or farther from each other overtime.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 4 Training Algorithm  
Currently, in terms of algorithm training, the simulation model of the photonic computing network is trained on the electronic computer. Then, the trained model parameters are loaded onto the photonic chip. However, even if it is trained in the electrical domain, its effect is still restricted by two aspects: the accuracy of the simulation model's description and the computing speed. The training algorithm includes forward propagation [42], finite difference calculation gradient (MIT), in situ back propagation [43] and gradient measurement (Stanford). These attempts have essentially been completed at the level of computer simulation, and have not been used for training on actual physical chips.  
The training algorithm problem of the photonic network is a restrictive factor for expanding the application of the PAIC. Photons cannot be stored like electrons. We cannot directly record the state of photons. Therefore, backward propagation algorithms that are widely used in electrical neural network training are difficult to directly transplant to photonic artificial intelligence network training. In order to solve this problem, [PERSON] et al. proposed an on-chip training algorithm in 2018 [43]. By recording the light field distribution and the phase distribution of the phase shifter, we can obtain the gradient value that decreases toward the convergence direction. Then, we calculate the phase configuration of the chip phase shifter in the next iteration to gradually converge to a better result. [PERSON] et al. trained a specific two-optical interference unit (OIU) neural network on the chip through simulation. It implements exclusive OR logic to verify the effectiveness of the algorithm.  
[PERSON] et al. proposed an effective training algorithm based on neuro-evolution strategy in 2019 [44]. It uses genetic algorithms (GA) and particle swarm optimization (PSO) to train the hyperparameters in ONNs and optimize the connection weights. The trained ONNs are used to complete the classification task for performance evaluation. The calculation results show that its accuracy and stability are sufficient to compete with  
Figure 9: SOA-based implementation of the nonlinear function. SOA is placed in the two arms of MZI, which mainly performs phase modulation. The input signal and the reference signal go through the 3 dB coupler, then are input into the two SOA for phase modulation. Cross-modulation is completed in SOA3.  
traditional learning algorithms. The system also uses the photonic artificial intelligence network to realize the classification of the modulation format of the communication signal. In the future, this algorithm can be further expanded and transplanted to larger-scale PAICs. It can gradually obtain the best configuration of the chip through on-chip training to complete specific functions. Figure 10 shows the results of autonomous learning using a gradient descent algorithm. It can be seen that with iterative learning, signal recovery is improving [42].
## 3 Results and Discussion
### Scan Rate Exploration  
The influence of the scan rate (v) on the hydroxylamine (100.0 uM) electrochemical response in 0.1 M PBS at pH = 7.0 on the PPy NTs/GSPE was determined via linear sweep voltammetry (LSV). The corresponding data are shown in Figure 6. The hydroxylamine oxidation peak current (Ipa) was enhanced with an increasing scan rate from 10 to 500 mV/s and the peak potential shifted towards a more negative value, confirming the irreversibility of the oxidation reaction of hydroxylamine on the PPy NTs/GSPE. The peak current of the hydroxylamine oxidation was proportional to the scan rate square root (v\({}^{1/2}\)) (Figure 6, inset), highlighting that the oxidation of hydroxylamine on the PPy NTs/GSPE was a typical diffusion-controlled electrode process, and the peak potential shifted towards a more negative value, confirming the irreversibility of the oxidation reaction of hydroxylamine on the PPy NTs/GSPE.  
The rate-determining step was explored via the Tafel plot (Figure 7) according to the rising domain of the current-voltage curve at a scan rate of 10 mV/s. This section of the voltammogram (or the Tafel region) is influenced by electron transfer kinetics between the hydroxylamine and the PPy NTs/GSPE surface. The Tafel slope was 0.2555 and the charge transfer coefficient (\(\alpha\)) was 0.77; thus, a two-electron transfer procedure can be discerned in the rate-determining step for the electro-oxidation of hydroxylamine.
## 3 Results
### Light Use Efficiency Parameter (\(\epsilon_{site}\)) and Eddy Covariance GPP  
The dynamics of GPP followed characteristic seasonal patterns in all years (Figure 5a), with an increase in both carbon assimilation and release associated with increasing air temperatures and incident PAR during the spring and summer months. The total EC-based annual GPP for 2018, 2019, and 2020 was 773.9 g C m\({}^{-2}\), 824.33 g C m\({}^{-2}\), and 775.7 g C m\({}^{-2}\), respectively. GPP also showed seasonal variability, with values for 2018 increasing to a maximum of 6.9 g C m\({}^{-2}\) on 28 June 2018, while the maximum GPP (6.6 g C m\({}^{-2}\)) was observed on 23 July 2019, and on 20 May 2020 (5.7 g C m\({}^{-2}\)).  
Significant seasonal variability was also observed in the LUE parameter with LUE values following the seasonal dynamics of temperature and radiation with the highest values during summer (Figure 5b). Values for 2018 ranged from 0.01 g C MJ\({}^{-1}\) in mid-February to 0.35 g C MJ\({}^{-1}\) in mid-June, returning to 0.01 g C MJ\({}^{-1}\) in December. The LUE parameter varied throughout the year from 0 to 0.41 g C MJ\({}^{-1}\) for 2019 and 2020, with peaks reaching at 0.41 g C MJ\({}^{-1}\) on 28 June 2019, and 0.36 g C MJ\({}^{-1}\) on 27 June 2020 (Figure 5b). The annual ecosystem scale value is derived for 2018, 2019, and 2020 using the Michaelis-Menten curve-fitting approach as 0.39, 0.42, and 0.38 g C MJ\({}^{-1}\) d\({}^{-1}\), with R\({}^{2}\) values of 0.71, 0.78, and 0.77, respectively.  
Figure 4: The seasonal temporal dynamics of meteorological parameters: (**a**) Average air temperature (\({}^{\circ}\)C), (**b**) Total daily precipitation (mm), (**c**) Water table depth (cm), and (**d**) Total daily PAR (M) m\({}^{-2}\) d\({}^{-1}\)).
# Stable Rules Definition for Fuzzy TS Speed Controller Implemented for BLDC Motor
## 4 Hardware Construction and Tests of the Stable Fuzzy Speed Controller
### Power Module  
Except for the control system, the second most essential part of a BLDC motor controller is the power module. In order to minimize the risk of damaging switches during the first tests, the decision to use IGBT transistors with high current persistence was made. To power the three-phase BLDC motor, the main inverter module consisted of three branches with two IGBT switches each (six IGBT transistors in total). A specific gate current and voltage with a specified edge were required to control the switches properly and avoid an undefined state for an IGBT's gate. To achieve this, an additional board with IR2301 IGBT drivers was created (Figure 14). The IGBT drivers transformed the PWM signal generated by the STM32 microcontroller into a corresponding signal with strictly specified values, which could be correctly interpreted by the gates of the transistors. Moreover, the drivers mentioned above ensured optoisolation between the power module and the control system. The cooling setup consisted of three small Raspberry Pi fans. Its role was to provide air flow between transistors. All of them were powered by 5 V DC.
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
## 4 Simulation Results and Analysis
### Ramp-Tracking Performance Analysis  
The trajectory of a radio telescope for star tracking can be considered a ramp curve, and its typical velocity is 0.0042 deg/s. So, a ramp signal with a slope of 0.004 deg/s is employed as the test signal. When performing ramp tracking, the position controllers shown above result in tracking errors related to the ramp signal's slope. An additional command-rate feedforward controller with a gain of 50 for LADRC and DLADRC, and a gain of 83.1 for RPID is used to eliminate the error.  
Figure 8 illustrates the results of the system's ramp response under the same wind disturbances, where Figure 8a,b show the results without and with command-rate feedforward, respectively. From Figure 8, it can be seen that the command-rate feedforward eliminates the static error during ramp tracking. The DLADRC-controlled system can quickly suppress the disturbance and track the ramp command. DLADRC can also achieve overshoot-free tracking with sharp steering in the command trajectory. The smooth response curves of the motor's input voltage and velocity in Figure 9 indicate that the active anti-disturbance approaches succeed in the motor velocity control. Comparing the errors in Table 4 with Table 3, it is easy to see that the ramp-tracking errors of the system controlled  
Figure 7: System response in an average wind of V = 6 m/s: (**a**) position of the base and dish, (**b**) motor’s velocity, (**c**) motor’s input voltage.  
by RPID, LADRC, and DLADRC under the same wind disturbance are almost the same as the pointing errors, indicating the wind effect on the system is constant and independent of the input signal.  
The simulation and analysis of the above four aspects all show that good rate-loop control performance is the basis of high-precision servo control. Using an active anti-disturbance controller in the velocity-loop can improve the system's response speed and reduce the peak magnitude and time of the system's response to disturbance. LADRC with the disturbance-derivative compensation can perform better initial dynamic and anti-disturbance performance than the original LADRC. A simple position proportional controller rather than other controllers can also achieve good position-tracking performance in the context of the active disturbance suppression-based velocity controller.  
Figure 8: System’s ramp response: (**a**) Without command-rate compensation and (**b**) with command-rate compensation, K = 50.  
Figure 9: Motor response: (**a**) Input voltage and (**b**) velocity.
## 3 Results  
### Evaluation of MERRA-2 SSR  
First, a comprehensive evaluation of MERRA-2 SSR, which is prerequisite for the evaluation of MERRA-2 GDB, is attempted in this section. The evaluation is made either on a total, i.e., all stations (Section 3.1.1) or on an individual station (Section 3.1.2) basis. It should be noted that apart from absolute SSR fluxes, SSR deseasonalized anomalies are also used in the correlations between the Reanalysis and stations fluxes in order to remove the significant contribution of the seasonal cycle of solar radiation.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 5 Conclusions  
In this study, theoretical derivation and numerical simulation are carried out to study the mechanical characteristics of a ring spring. The original formula (Equations (1) and (2)) only applies to thin-walled ring springs, the bearing capacity of which is relatively weak. Hence, a new formula suitable for thick-walled ring springs has been put forward. Then, a novel ring spring isolator was designed for vibration isolation of metro superstructures. The following conclusions can be drawn:  
(1) Through the comparison of theoretical calculation and finite element simulation, the error of the supplementary formula of the maximum hoop tensile stress for thick-walled ring springs is within 8%, and it becomes more accurate with the increase in the ratio of thickness to diameter.  
(2) From the simulation results of the thick-walled ring spring, the stress of the thick-walled ring spring changes simultaneously along the direction of the diameter. The maximum stress of the outer ring occurs at the cone angle whereas the maximum stress of the inner ring occurs on the innermost section. The energy dissipation capacity of the ring spring increases along with the increase in the loading amplitude.  
(3) A ring spring is a kind of vibration damping device widely used in the field of mechanical engineering. In this study, an innovative application of ring springs is used to design vibration isolators for metro superstructures.  
(4) By comparing the dynamic response of isolated structures and non-isolated structures, it is found that the isolator significantly reduces the vertical acceleration of the structure. The amplitude of vertical acceleration is reduced by 50% while the vibration isolation rate is up to 60.53%. The average vibration isolation rate is 37.73%. The isolator changes the main frequency of vibration, which greatly reduces the vibration frequency by 30 Hz to 50 Hz.
_applied_  
_sciences_  
_Article_  
**Investigation of Volatile Compounds in Combination with Multivariate Analysis for the Characterization of Monofloral Honeys**  
**[PERSON] \({}^{\ast}\)\({}^{\copyright}\), [PERSON], [PERSON] and [PERSON]**  
Laboratory of Agriculture-Seriatric, Aristotle University of Thessaloniki, 57001 Thessalonik, Greece;  
[EMAIL_ADDRESS] (V.L.); [EMAIL_ADDRESS] (D.K.); [EMAIL_ADDRESS] (M.A.R.)  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +30-231-0991-753  
**Abstract:** Lately there has been a growing demand for monofloral honeys with distinctive properties. Considering the limitations of pollen analysis, the volatile profile of honey has been proposed as a helpful supplementary tool for the confirmation of monoflorality; however, research remains regarding the volatile markers that may characterize the monofloral honey types. Therefore, in this study, we tried to expand the research by investigating the aroma profiles of five monofloral honey types (fir, pine, erica, thyme, cotton) and discriminate them through chemometric approach. A purge and trap-gas chromatograph-mass spectrometer system was used for the extraction, separation, and identification of volatile and semi-volatile compounds. Thyme honey had the richest quantitatively aroma profile, with 97 volatile compounds, whereas fir and cotton honeys had 65 and 60 volatile compounds, respectively. From a total of 124 compounds, the 38 were detected in all the studied honey types. Thyme honey was distinguished by the presence (or percentage participation) of benzeneacetaldehyde, benzealdehyde, and benzyl nitrile; erica honey of isophorone and furfurral; cotton honey of 1-butanol, 2-methyl, 1-pentanol, and 4-methyl-; and honeydew honeys of \(\alpha\)-pinene, octane, and nonanal. The discriminant analysis confirmed that the percentage participation of volatile compounds may lead to the discrimination of the studied monofloral honey types.  
**Keywords:** volatile compounds; volatile profile; monofloral honeys; discriminant analysis
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## 7 Numerical Results
### Problem Description  
The matrices used for the numerical tests are generated using values of \(n\) from 10 to 320. In particular, Table 1 illustrates all of the matrices used, showing their dimension and number of nonzero entries, while Figure 5 shows the sparsity pattern the matrix M10.  
The number of nonzero entries per row vary between 14 and 18 and the density increases as \(n\) grows. The smaller matrices M10 and M20 will only be used in the layer of the Multigrid preconditioner, while the other will be actually used as matrices for the saddle point system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Matrix** & **Dimension** & **NNZ** \\ \hline M10 & 1003 & 14,280 \\ M20 & 3803 & 62,008 \\ M40 & 14,803 & 258,268 \\ M80 & 58,403 & 1,054,076 \\ M160 & 232,003 & 4,258,388 \\ M320 & 924,803 & 17,118,310 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the matrices.  
Figure 4: Velocity direction for the Stokes problem.  
Figure 3: Velocity magnitude for the Stokes problem.  
Concerning the Navier-Stokes equations, the matrices used are obtained after five iterations of a Picard scheme, which ensures that the nonlinear phenomena are well represented. The value of the viscosity is set to 1 for the Stokes problem. For the Navier-Stokes equations, it will assume the values \(10^{-1},10^{-2},5\cdot 10^{-3}\), and \(10^{-3}\). Lowering the viscosity, the system will become more asymmetric, which is expected to worsen the properties of the preconditioner.  
The numerical results will regard the Navier-Stokes equations, for which we will be analyzing both the spectral properties and number of iterations required for the GMRES to converge. It is worth pointing out that the scalability of the results would not change using another Krylov subspace solver, e.g., BICGSTAB, since the preconditioner is derived based on the properties of the underlying problem, and not considering the specific solver used. The notation will be the following: \(\alpha_{F}\) and \(\beta_{F}\) represent the maximum and minimum eigenvalues in modulus of the preconditioned \((1,1)\) block; \(\alpha_{S}\) and \(\beta_{S}\) represent the maximum and minimum eigenvalues in modulus of the preconditioned Schur complement (obviously, the zero eigenvalue of the Schur complement is not considered, since it is always present).
# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
###### Abstract  
As the cornerstone of terrestrial ecosystems, forests have faced mounting challenges due to escalating human activities, jeopardizing their vital ecological functions and even their existence. It has become an important issue to explore how to promote harmonious coexistence of man and nature, or even to improve the forest ecological function (FEF) through human activities. Thus, in this study, we select the Yellow River Basin (YRB) in China as a typical region. Firstly, we assess the FEF at the county level and reveal their spatial distribution and agglomeration characteristics on the basis of the data from the Ninth National Forest Inventory of China. Then, using multiple linear regression (MLR) and geographically weighted regression (GWR) modeling, we further explore the overall impacts of different human activities on FEF and their spatial differences, respectively. Our findings underscored a moderate deficiency in the county-level FEF in the YRB, with pronounced positive spatial agglomerations. The high-high areas are primarily clustered in the southern and central mountainous areas, whereas low-low areas are distributed in the upstream warm temperate steppe and desert-grassland regions. Human activities exert substantial impacts on FEF, with distinct spatial heterogeneity in the coefficient and significance levels. The trend analysis indicates that FEF is more sensitive to the increase in living land, population density and forest protection in the east-west direction. And in the north-south direction, FEF is more easily affected by agricultural development, population growth and urbanization. This study verifies that natural factors dominate FEF in those regions where human activities are quite scarce, and also reveals that due to the inter-constraint or counteract effects among different human activities, FEF may still ultimately depend on the natural endowments in some populated regions. We point out the core human activity factors affecting FEF after excluding the interference from natural conditions. And we recommend that policymakers prioritize sustainable development strategies that mitigate the adverse impacts of human activities on forest ecosystems while promoting conservation efforts tailored to the unique characteristics of each region.  
forest ecological function; human activities; spatial correlation; geographically weighted regression +
Footnote †: journal: Applied sciences  
0000-0002-2000-0002-0000][PERSON]  
1 Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing 100091, China; [EMAIL_ADDRESS] (Q.W.); [EMAIL_ADDRESS] (Y.D.)1
## 3 FEM Modelling
### Concrete Material Model  
Figure 8 shows the uni-axial stress-strain relationship [21] for unconfined concrete used in this model. The concrete cylinder compressive strength\(f_{c}\) = 26.7 MPa was obtained experimentally, while the corresponding strain \(\epsilon_{c}\)' was taken as 0.003.  
The value of the proportional limit stress was taken as 0.5\(f_{c}\)', and the initial modulus of elasticity of the concrete was calculated by Eurocode 2 [22]:  
\[E_{c}=22,000\cdot\left[\frac{f_{c}^{\prime}+8}{10}\right]^{0.3} \tag{2}\]  
The nonlinear stress-strain relationship of the concrete for compression was determined from the relation proposed by [PERSON] [23]:  
\[\sigma=\frac{E_{c}\cdot\epsilon_{c}}{1+(R+R_{E}-2)\cdot\left(\frac{\epsilon_{ c}}{\epsilon_{c}^{\prime}}\right)-(2\cdot R-1)\cdot\left(\frac{\epsilon_{c}}{ \epsilon_{c}^{\prime}}\right)^{2}+R\cdot\left(\frac{\epsilon_{c}}{\epsilon_{ c}^{\prime}}\right)^{3}} \tag{3}\]  
where \(R\) and \(R_{E}\) were computed using the following equations:  
\[R_{E}=\frac{E_{c}\cdot\epsilon_{c}^{\prime}}{f_{c}^{\prime}} \tag{4}\]  
Figure 8: The constitutive model for concrete.  
Figure 7: Three-dimenional model of CFT column in ABAQUS.  
And  
\[R=\frac{R_{\varepsilon}\cdot(\ R_{\varepsilon}-1)}{(\ R_{\varepsilon}-1)^{2}}- \frac{1}{R_{\varepsilon}} \tag{5}\]  
In Equations (4) and (5), \(R_{\varepsilon}\) and \(R_{\varepsilon}\) are equal to 4, as recommended by [PERSON] and [PERSON] [24]. The tensile strength of the concrete was approximated as 9% of concrete compressive strength. The tensile stress reduces to zero at a total strain of 0.001 [24].  
The concrete was modelled using the concrete damaged plasticity model in ABAQUS with an expansion angle of 20\({}^{\circ}\). The Poisson coefficient is 0.2 [25; 26; 27].