esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurately predicting soil respiration (R\({}_{\text{s}}\)) has received considerable attention recently due to its importance as a significant carbon flux back to the atmosphere. Even small changes in R\({}_{\text{s}}\) can have a significant impact on the net ecosystem productivity of forests. Variations in R\({}_{\text{s}}\) have been related to both spatial and temporal variation due to changes in both abiotic and biotic factors. This study focused on soil temperature and moisture and changes in the species composition of the overstory and understory and how these variables impact R\({}_{\text{s}}\). Sample plots consisted of four vegetation types: eastern hemlock (_Tsuga canadensis_ L. Carriere)-dominated overstory, mountain laurel (_Kalma latifolia L_)-dominated understory, hardwood-dominated overstory, and cinnamon fern (_Osmundastrum cinnamomeum_ (L) [PERSON]. [PERSON])-dominated understory, with four replications of each. Remotely sensed data collected for each plot, light detection and ranging, and hyperspectral data, were compiled from the National Ecological Observatory Network (NEON) to determine if they could improve predictions of R\({}_{\text{s}}\). Soil temperature and soil moisture explained 82% of the variation in R\({}_{\text{s}}\). There were no statistically significant differences between the average annual R\({}_{\text{s}}\) rates among the vegetation types. However, when looking at monthly R\({}_{\text{s}}\), cinnamon fern plots had statistically higher rates in the summer when it was abundant and hemlock had significantly higher rates in the dormant months. At the same soil temperature, the vegetation types' R\({}_{\text{s}}\) rates were not statistically different. However, the cinnamon fern plots showed the most sensitivity to soil moisture changes and were the wettest sites. Normalized Difference Lignin Index (NDLI) was the only vegetation index (VI) to vary between the vegetation types. It also correlated with R\({}_{\text{s}}\) for the months of August and September. Photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), and normalized difference nitrogen index (NDNI) also correlated with September's R\({}_{\text{q}}\). In the future, further research into the accuracy and the spatial scale of Vis could provide us with more information on the capability of Vis to estimate R\({}_{\text{s}}\) at these fine scales. The differences we found in monthly R\({}_{\text{s}}\) rates among the vegetation types might have been driven by varying litter quality and quantity, litter decomposition rates, and root respiration rates. Future efforts to understand carbon dynamics on a broader scale should consider the temporal and finer-scale differences we observed.  
carbon flux; hardwood forest; soil moisture; soil temperature; vegetation indices; vegetation types +
Footnote †: journal: Physics Letters Letters
## 3 Nonlinear Dynamic Analysis of the RBRS  
In the previous section, the dynamics model of the rotor-bearing-runner system (RBRS) is introduced and the system dynamics equation is obtained. In this section, the influence of different system parameters on nonlinear systems under the coupling of mechanical unbalance force, nonlinear magnetic pull force, hydraulic unbalance force, and nonlinear rubbing force is studied, through Bifurcation diagram, 3-D frequency spectrum, Waveform, Frequency, Axis orbit, and Poincare map. The parameters for the coupling system are presented in Table 1. In addition, the detailed simulation condition schematic is displayed in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(m_{1}\), \(m_{2}\) (kg) & \(5.7\times 10^{4}\), \(5.2225\times 10^{4}\) \\ \(k_{z2v}\), \(k_{z3v}\) (N/m) & \(1.44\times 10^{8}\), \(0.34\times 10^{8}\) \\ \(k_{z2y}\), \(k_{z3y}\) (N/m) & \(3.71\times 10^{8}\), \(1.92\times 10^{8}\) \\ \(c_{z2x}\), \(c_{z3v}\) (N-s/m) & \(6.5\times 10^{3}\), \(3\times 10^{3}\) \\ \(c_{z2y}\), \(c_{z3y}\) (N-s/m) & \(6.5\times 10^{3}\), \(3\times 10^{3}\) \\ \(\xi_{1}\), \(\xi_{2}\) & \(0.03\), \(0.03\) \\ \(l\mu_{0}\) & \(4\pi\times 10^{-7}\) \\ \(\alpha\) & \(1.3\) \\ \(K_{j}\) & \(5.1\) \\ \(K_{w}\) (N/cm) & \(1.63\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters of RBRS.
# Improved Interpolation and Anomaly Detection for Personal PM\({}_{\mathbf{2.5}}\) Measurement
## 2 Methods  
### Proposed Algorithm  
In this paper, we proposed two algorithms: the kernel regression-based interpolation method and the subsequent abnormal-data detection method. The algorithm presented in this paper was done in the order shown in Figure 1. First, we chose the part of the total data that had no missing data and estimated the bandwidth for the chosen data part. Here, the bandwidth was the value to use for the kernel regression-based interpolation (KRBI) (dotted line in Figure 1), which will be explained in the next subsection. Afterwards, we examined missing data for the entire dataset. If there was missing data, we used linear interpolation (LI) to interpolate the missing data. Afterwards, the interpolation was done again by applying the KRBI algorithm [14]. In this case, the optimal bandwidth value, which was previously obtained, was used. If there were no missing data, abnormal-data detection began for the entire dataset.
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 2 Rough Surfaces Parametrization
### Generation Process of an Individual Gaussian Rough Surface  
The method of generating these surfaces is described in detail in [6; 27]. It first generates uncorrelated random numbers with a Gaussian distribution of zero mean and specified RMS value. Then, it performs a moving average with weights that ensure that the correlation function is Gaussian with the desired correlation length. The following weights yield a surface with two independent lengths \(L_{x}\) and \(L_{y}\) in the two perpendicular directions:  
\[W_{ij}\ =e^{-2[(i\Delta x)^{2}/L_{x}^{2}+(i\Delta y)^{2}/L_{y}
## 2 Materials and Methods
### Long Short-Term Memory (LSTM)  
The LSTMs depicted in Figure 6 are a variant of recurrent neural networks (RNNs). RNNs can process sequential data and maintain information over time, but basic RNNs need help with problems such as vanishing and gradient bursting, which complicate their ability to capture long-term dependencies [49; 50]. Therefore, LSTMs were developed, which incorporate forget, input, and output gates, and an internal memory unit called the cell state. The forget gate is responsible for removing irrelevant information, the input gate updates the cell state with new information, and the output gate filters the current state to transmit the most important information. LSTMs has been shown in several  
Figure 5: CNN 1D input data format.  
papers to be highly effective for accurate forecasting in a variety of applications, including renewable energy.  
The forget gate is calculated by the sigmoid function applied to the linear combination of the previous hidden state \(h_{t-1}\) and the current input \(x_{t}\), as shown in Equation (14).  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{14}\]  
Next, the input gate is determined in a similar manner, as shown in Equation (15).  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{15}\]  
The new candidate information for the memory cell is calculated using the hyperbolic tangent function (tanh), as shown in Equation (16).  
\[\tilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{16}\]  
The state of cell \(C_{t}\) is updated by combining the old information, modulated by the forgetting gate, with the new candidate information, weighted by the input gate, as described in Equation (17).  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\tilde{C}_{t} \tag{17}\]  
The output gate, which determines the part of the cell that should be passed to the next hidden state, is defined by Equation (18).  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{18}\]  
Finally, the new hidden state \(h_{t}\) is calculated by applying the function tanh to the cell state and multiplying by the gate output, as presented in Equation (19).  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{19}\]  
where, \(W_{f}\), \(W_{i}\), \(W_{C}\), and \(W_{o}\) are the weights associated with each gate, while \(b_{f}\), \(b_{i}\), \(b_{C}\), and \(b_{o}\) are the corresponding biases. The \(\sigma\) function represents the sigmoid activation that regulates the flow of information in the gates, while tanh is used to generate the new memory units \(C_{t}\) due to its ability to capture nonlinear relationships efficiently.  
Figure 6: Process of LSTM network.
# The Effect of Task Complexity on Time Estimation in the Virtual Reality Environment: An EEG Study
## 3 Results
### The Effect of Task Complexity and Block Sequence on Dependent Variables
#### 3.2.2 Relative Beta-Band Power of EEG  
The repeated-measures ANOVA results revealed a significant effect of task complexity on the relative beta-band power at Fz (\(F\) (1, 98) = 5.26, \(p\)! 0.05) and Pz (\(F\) (1, 98) = 5.85, \(p\)! 0.05) and a marginal effect on the relative beta-band power at Cz (\(F\) (1, 98) = 2.92, \(p\) = 0.09). Figure 4 shows the interaction plots of the two independent variables with the relatively high beta-band power at Fz (a), Pz (b), and Cz (c). The relative beta-band power at the three electrodes decreased with task complexity. The block sequence presented no significant results, indicating that task repetition did not affect the fraction of beta-band power. There was no significant interaction effect between task complexity and block sequence on each signal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Resposes**} & \multicolumn{2}{c}{**Low Complexity**} & \multicolumn{2}{c}{**High Complexity**} \\ \cline{2-5}  & **Mean** & **95\% CI** & **Mean** & **95\% CI** \\ \hline Task completion time & 47.74 & [45.00, 50.47] & 140.69 & [130.98, 150.40] \\ Estimated time & 56.78 & [50.32, 63.24] & 168.07 & [151.70, 184.44] \\ Absolute time estimation error & 18.95 & [14.92, 22.99] & 56.87 & [46.86, 66.89] \\ NASA-TLX score & 3.35 & [2.91, 3.79] & 5.56 & [5.04, 6.08] \\ Relative beta-band power at Cz & 0.2 & [0.19, 0.20] & 0.19 & [0.18, 0.20] \\ Relative beta-band power at Fz & 0.18 & [0.17, 0.19] & 0.17 & [0.16, 0.18] \\ Relative beta-band power at Pz & 0.2 & [0.19, 0.21] & 0.19 & [0.19, 0.20] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the measurements.
## 4 Results
### Characteristics of Sea Surface Temperature Errors  
#### 4.4.1 Dependence of SST Errors on Wind Speed  
The vertical structure of water temperature in the upper ocean is affected by solar radiation, wind speed, ocean current system, and other variables. Especially under low wind speeds and high insolation condition, the diurnal thermocline of the upper ocean can develop significantly [63; 64]. The satellite infrared sensors observe the skin temperature at a depth of about 10 \(\upmu\)m [39; 65], while the buoys measure the water temperature at about 1 m depth. Therefore, the errors of satellite-derived SSTs with respect to the buoy temperature may represent different characteristics depending on the wind speed.  
Figure 5: Comparison between MCSSTs and in situ temperatures during the (**a**) daytime and (**b**) nighttime and between NLSSTs and in situ temperatures at (**c**) daytime and (**d**) nighttime, where the color represents the percentage of the data to the total number of matchup points in a bin of \(0.5\times 0.5\,^{\circ}\)C. Bias, root mean square error (RMSE), scatter index (SI), and correlation coefficient (R) are given in each plot.  
In Figure 7, each panel displays a SST difference between COMS/MI and drifting buoys, as a function of wind speed from SSMIS. Both daytime and nighttime MCSST errors do not exhibit an obvious trend, as shown in Figure 7a,b. The NLSST errors also show similar characteristics to those of MCSST, with a relatively small standard deviation (Figure 6c,d). The SST calculated in this study represents the water temperature between 10 \(\upmu\)m and several tens of cm, as coefficients were derived by applying the regression method to the drifting buoy temperature observed at about 20 cm depth. Strictly speaking, the SSTs derived from COMS/MI measurements in this study could not be called the skin temperature, and the drifting buoy temperatures of about 20 cm depth are also not a bulk temperature independent of the diurnal variations. For these reasons, the errors of COMS/MI SST in Figure 7 appear to be insensitive to wind speed, unlike the characteristics of skin-bulk temperature difference with respect to the wind speed analyzed by [PERSON] et al. [43; 63] and [PERSON] [64]. This implies that the present SST coefficients can be applied continuously for COMS/MI data without paying much attention to the effect of wind.  
Figure 6: Spatial distribution of mean differences between MCSSTs and in situ temperatures for the (**a**) daytime and (**b**) nighttime and between NLSSTs and in situ temperatures in the (**c**) daytime and (**d**) nighttime in bins of \(5^{\circ}\times 5^{\circ}\) for the period from April 2011 to March 2014.
# Fundamental Spacetime Representations of Quantum Antenna Systems
## 7 Conclusions  
We developed foundations for quantum antenna theory using the relativistic framework of quantum field theory (QFT) with emphasis on the source-sink (transmitter-receiver) model for potential applications to quantum communication systems. The QFT-based q-antenna is the most general type of antenna systems because it includes both the classical and quantum-optics-based q-antennas as special cases. In addition, relativistic q-antennas can accommodate a wider range of physical processes, since the same theory may deal with massive or massless particles and for different spins. These types of q-antennas, the most general to date, appear to enjoy the distinction of exhibiting a current Green's function that is itself a quantum field, which we dubbed before the quantum source of the antenna system, i.e., the field \(\phi(x)\) as per (49). We have illustrated the theory mostly with the specific example of the neutral Klein-Gordon field because of its simplicity and importance for building the general solution to other, more complex problems. In particular, we have shown how in the linear quantum antenna model, one may directly construct quantum radiation directivity and gain measures that somehow resemble their counterparts in classical antenna theory. A future research based on the present theory would proceed to construct more concrete radiation models for quantum fields such as the Procafield (massive electromagnetism), the Maxwell-Dirac field (quantum electrodynamics), the graviton field (gravitational antennas), and possibly other types. Then, the present paper attempted to provide a first sketch of a possible general theory that may blend all these distinct phenomena, where our key idea was that relativistic QFT supplies precisely a promising such unifying theoretical framework.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} QFT & Quantum field theory \\ QM & Quantum mechanics \\ SR & Special relativity \\ c-antenna & Classical antenna \\ q-antenna & Quantum antenna \\ q-radiation & Quantum radiation \\ q-state & Quantum state \\ q-source & Quantum source \\ c-source & Classical source \\ EM & Electromagnetic/Electromagnetics \\ ACGF & Antenna current Green's function \\ \end{tabular}
## 3 Experiments and Functioning Tests
### Leaky Faucet Experiment  
In this experiment, the goal is to capture the vibrations in each case of the leaky faucet, running faucet, and watertight faucet to investigate whether the data from these cases are distinguishable to assist in detecting the leaky faucet in the home maintenance framework. For this purpose, the piezoelectric patch is used to sense the vibrations of the water droplets in each case, and the data acquisition is performed using an Arduino Uno board by reading the analog input data of the piezoelectric patch.  
Additionally, in the case of the leaky faucet, a test is performed to investigate the capability of the AR headset in providing guidance steps using a QR code including thelinks and information to repair the faucet while the user is performing the repair and seeing the holographic guide in AR.
# Multi-Modal X-ray Imaging and Analysis for Characterization of Urinary Stones
## 3 Results
### X-ray Speckle-Based IMAGING (SBI)  
Figure 4 represents the multi-modal radiographs retrieved from the XST-SBI technique for three stone samples.  
As observed from Figure 4, in all cases, the phase-contrast and dark-field images can provide more details about the heterogeneity, porosity, and roughness of the stone structure, added to the density and absorption information provided by the attenuation images. The dog's stone appears very smooth in structure in the attenuation and refraction images, except for some larger central cracks and porosity visible in the refraction images, as also observed in the microCT slices. However, the dark-field image of this sample additionally shows high-frequency signals related to the micro-porosities or large imperfections inside the crystalline stone, which can not be observed in the other two contrast images.  
Figure 4: Multi-contrast radiographs extracted from dog, cat, and human urinary stones. (**a**) Horizontal refraction angle, (**b**) vertical refraction angle, (**c**) attenuation, and (**d**) dark-field contrast.  
Generally, the dark-field radiographs reveal a high signal from the microstructures and micro-porosities inside the urinary stones, due to the strong X-ray scattering from them (Figure 4d). Such micro-heterogeneities are below the spatial resolution of the imaging system and can not be visualized in the attenuation or refraction images. The strongest dark-field signals are observed for the human stone, which was known to have a layered and very non-uniform structure (Figure 4d--bottom). This heterogeneity can be also observed through the refraction images of the human stone as high-frequency refraction signals (Figure 4a,b--bottom). The cat's stone shows some degree of irregularity and porosity in some areas, which has generated strong signals both in refraction and dark-field images, as confirmed by microCT images to be mostly surface roughness. Other than this, the cat's stone exhibits more uniformity in the multi-contrast radiographs than the human sample.
## 2 Methods
### Ground Data Collection  
Twelve elementary sampling units (ESUs) of 30 m by 30 m to represent the Landsat spatial resolution were sampled. Coordinates were recorded at each ESU with a Global Positioning System (GPS) handheld receiver unit e-Trex (GARMIN International, Inc), with \(<\)15 m accuracy. At the center of each ESU two or three trees were sampled, according to species richness. To take account of the uneven distribution of CC in the canopy [58], on each sampled tree 15 leaves were taken from the top of the canopy and 15 leaves from below the top. The following measurements were performed for each tree:  
1. Leaf hyperspectral measurements: Leaf adaxial spectra were measured using the leaf clip of the Analytical Spectral Devices (ASD) Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA) with a 350-2500 nm spectral range. Three scans were taken per leaf. The ASD spectrum averaging per scan was set to 25. Optimization and dark current collection were performed before measuring each leaf.  
2. Leaf Chlorophyllyll: All leaves used in (i) were measured using a portable Minolta SPAD-502 Chlorophyll Meter (SPAD-502). The average of five SPAD readings evenly distributed around the leafaxial face was taken to represent the CC. Calibration equations were used to convert the SPAD values to actual chlorophyll concentration. Finally, the ESU CC was expressed as the average of the total number of leaves measured per ESU. For example, if in one ESU the total number of leaves measured were 90, the chlorophyll concentration would be the average of 90 leaves.
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 2 System Model and Preliminaries  
Throughout our discussion, we focus on the uplink of a wireless satellite system which supports two distinct services, labeled as \(\mathcal{I}_{\text{a}}\) and \(\mathcal{I}_{\text{b}}\). The former grants specific requirements in terms of data-rate and error probability, and is embodied in our setting by a single user, \(\mathsf{s}_{\text{a}}\), which sends data to the receiver under an average power constraint, \(\rho_{\text{a}}\) whenever it is granted access to the medium. On the other hand, \(\mathcal{I}_{\text{b}}\) foresees that a large number of terminals share the wireless channel under a slotted ALOHA contention policy [33] to attempt packet delivery in a best-effort fashion, e.g., for mMTC. Specifically, over any time slot allotted to \(\mathcal{I}_{\text{b}}\), a variable number of users \(U\) access the channel, each transmitting toward the receiver for the whole slot duration with average power \(\rho_{\text{b}}\). We note that, while the single user for the transmission of a data packet is subject to an average power constraint \(\rho_{\text{b}}\), the _overall_ power injected in the system by service \(\mathcal{I}_{\text{b}}\) varies both with the number of terminals accessing the channel over a single slot and the number of allocated slots. Such a working assumption is representative of the practical implementations of machine-type communications. Following a common approach, \(U\) is modeled as a Poisson r.v. of parameter \(\lambda\), independent and identically distributed across slots, so that the following holds:  
\[p_{U}(u)=\frac{\lambda^{u}e^{-\lambda}}{u!}.\]  
The assumption is especially representative for traffic generated by a multitude of low duty cycle nodes monitoring and/or reporting data generated by heterogeneous systems and phenomena [34], as often encountered in mMTC satellite systems. Furthermore, we consider for \(\mathcal{I}_{\text{b}}\) no feedback on the outcome of sent packets nor retransmission policies. This approach is in line with the best-effort nature of many IoT applications, where packets losses may be tolerated, awaiting the delivery of a successive update from the monitored devices.  
To characterize the performance of the system, we tackle two operation modes, considering an _orthogonal_ and an _overlay_ allocation for \(\mathcal{I}_{\text{a}}\) and \(\mathcal{I}_{\text{b}}\). In both cases, having in mind the uplink of a satellite system, we assume for the link between a transmitter and the receiver a line-of-sight connection with perfect power control. Accordingly, the channel coefficient at the receiver is a constant and known value for all users, assumed to be unitary for the sake of simplicity, and all users of a given service are received with the same power level.
# Order-Based Identification of Bearing Defects under Variable Speed Condition
## 3 Validation of the Proposed Features
### Results and Discussion  
As mentioned in Section 3.1, the datasets considered in this work consist of 60 vibration signals of 10 s each corresponding to bearings with different health states operating under variable operating speed conditions.  
Preprocessing and feature extraction processes are further clarified in this section using the vibration signal shown in Figure 3. The latter corresponds to the vibration data collected from a faulty bearing with an outer race defect operating under a rotational speed decreasing from 25.4 Hz to 10.3 Hz.