esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Climate and Fruit Growth Resulted in the Discrepancies in Volatiles in Grapes  
Grape volatiles have been widely studied and classified into four groups according to metabolic pathways, including the derivatives of amino acids, phenylpropanoids/benzenoids and fatty acids, as well as terpenoids [22]. As recorded, terpenoids are the most important volatiles for grape flavor, and the monoterepene subtypes are the most abundant volatiles in grape berries [22]. Similarly to the former study [22], fruit ripening changed the terpenoid composition in 'Red Globe' grapes in our study. However, although (E)-2-hexenal has previously been detected as a principal component in grapes, it accumulated during fruit ripening [23], in contrast to the decreasing patterns of (E)-2-hexenal in TS and US in  
Figure 3: The validation of TFs at specific stages. Genes that expressed more highly in one of the samples (TS or US) are depicted on that side.  
our result. The diverse patterns might be influenced by the growth environments, as the previous studied grapes were cultivated in Northern China, where they enjoyed the temperate continental monsoon climate. Incidentally, as it emits a green leafy flavor [23], the decline in (E)-2-hexenal during ripening in the present study might improve the edible flavor of 'Red Globe' grapes. Apart from the consequences of ripening, volatiles also changed according to growing conditions. A previous experiment conducted by bagging grapes with different cover colors during ripening periods, indicated the influence of light conditions on decanal [24]. Thus, the higher decanal content in TS than in US indicated its light-enhanced characteristic in the present work. Similarly, terpenoids in tea may be subject to a hypothetical regulatory mechanism, as mature miRNAs maintained by light regulators may negatively regulate terpenoid biosynthesis [25]. Hence, in our results, the consistently lower contents of terpenoids in TS, such as \(\alpha\)-cadinene, \(\delta\)-amorphene, \(\alpha\)-muvurolene and \(\alpha\)-calacorene, might indicate the effects of negative regulation by light intensity.
## 3 TanSat Simulations and Retrieval Assumptions  
The application of the theory introduced in the preceding section initially requires measurement and state vector definitions, as well as the error covariance matrices. Based on the characteristics of the ACGS onboard TanSat, we used a forward model to simulate the ACGS spectra in three NIR bands and their Jacobians with respect to each element in the state vector. For the TanSat, there are three observation modes with different goals: nadir mode viewing nadir target, which is used for over-land observations; sun-glint mode, which looks at the sun glint to obtain more reflected radiance over the water body with low surface reflectance; and target mode, which is aimed at single point observations with different angles. In this research, we only focus on CO\({}_{2}\) retrieval in nadir mode over land, so the satellite viewing zenith angle (VZA) is maintained at 0\({}^{\circ}\) (nadir), and the scattering angle varies from only several SZAs. In this section, we briefly describe the instrument characteristics and the forward model, particularly the details of the aerosol models. Subsequently, the necessary assumptions regarding the _a priori_ and measurement error covariance matrices are presented.
## 3 Method and Results
### Attribute Generation and Classification  
Seismic attributes were calculated from spectrally broadened data and classified into four groups, in which Group 1 is a set of conventional and well-known attributes, as listed below [1]:  
* amplitude envelope;
* amplitude weighted cosine phase;
* amplitude weighted frequency;
* amplitude weighted phase;
* apparent polarity;
* average frequency;
* cosine of the instantaneous phase;
* derivative;
* derivative instantaneous amplitude;
* dominant frequency;
* energy;
* filter 05/10 15/20;
* filter 15/20 25/30;
* filter 25/30 35/40;
* filter 35/40 45/50;
* filter 45/50 55/60;
* instantaneous frequency;
* instantaneous phase;
* integrate;
* integrate absolute amplitude;
* x-coordinate;
* y-coordinate;
* quadrature trace;
* second derivative;
* second derivative instantaneous amplitude; and
* semblance.  
Time Group 2 includes all attributes from Group 1 in addition to a relative geologic age attribute (inline shown in Figure 4), which was obtained by building a volume of several interpreted seismic horizons and interpolating the fractional position between horizons.  
Figure 4: Inline example of relative geologic age volume. Each color represents a different seismically inferred relative geologic age.  
Figure 4 shows an inline of the relative geologic age volume.  
Constrained least squares spectral analysis (CLSSA) as presented in [8] is a spectral decomposition technique that achieves better time-frequency resolution than the commonly used the Fourier transform and continuous wavelet transform (CWT) methods. In this study, CLSSA was implemented to extract the amplitude, phase, and magnitude components from spectrally broadened seismic data every 10 Hz from 10 to 130 Hz. Group 3 includes all attributes from Group 1 in addition to volumes of amplitude, magnitude, and phase components at each selected frequency from the CLSSA spectral decomposition. The cosine of the phase component at each frequency was also included.  
Figure 5 shows a section of amplitude, magnitude, and phase for the isofrequency volume at 100 Hz.  
Group 4 includes all attributes in Groups 1 to 3.  
Figure 5: (a) Amplitude at 100 Hz (polarity: an increase in amplitude corresponds to an increase in acoustic impedance), (b) magnitude at 100 Hz, and (c) phase at 100 Hz.
# EPO Extension of Dispatching Rules to Minimize Effects of Time Uncertainty in Production Scheduling
## 4 Case Study
### Results
\begin{table}
\begin{tabular}{c c c c} \hline
**Days** & **Sample** & \(\textit{LPT}_{\textit{A}}\)-\(\textit{LPT}_{\textit{T}}\) & \(\textit{EPO}_{\textit{A}}\)-\(\textit{EPO}_{\textit{T}}\) \\ \hline \multirow{3}{*}{5} & 1 & 4.5 & 3.3 \\  & 2 & 2.7 & 1.4 \\  & 3 & 8.8 & 8.4 \\ \hline \multirow{3}{*}{10} & 1 & 5.1 & 2.1 \\  & 2 & 3.0 & 2.8 \\  & 3 & 14.6 & 14.1 \\ \hline \multirow{3}{*}{20} & 1 & 4.3 & 4.2 \\  & 2 & 4.7 & 3.6 \\ \cline{1-1}  & 3 & 7.1 & 7.1 \\ \hline \multicolumn{2}{c}{Avg} & 6.1 & 5.2 \\ \hline \end{tabular}
\end{table} 
Table 4: Difference between theoretical and actual values for analyzed dispatching rules (\(C_{max}\)). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Days** & **Numb.** & \(\textit{LPT}_{\textit{T}}\) & \(\textit{EPO}_{\textit{T}}\) & \(\textit{LPT}_{\textit{A}}\) & \(\textit{EPO}_{\textit{A}}\) \\ \hline \multirow{3}{*}{5} & 1 & 23.9 & **23.8** & 26.9 & **26.7** \\  & 2 & 25.7 & 25.7 & **28** & 28.3 \\  & 3 & 27.7 & **27.6** & 34.6 & **33.6** \\ \hline \multirow{3}{*}{10} & 1 & 35.3 & **35.1** & 40.2 & **40** \\  & 2 & 38.4 & **38.3** & **42.8** & 43 \\  & 3 & **35.4** & 35.5 & 41.4 & **41** \\ \hline \multirow{3}{*}{20} & 1 & 51.5 & **51** & 58 & **56.2** \\  & 2 & **47.9** & 49.2 & **54.7** & 55 \\ \cline{1-1}  & 3 & 33.1 & **32.9** & 39 & **37.2** \\ \hline \end{tabular}
\end{table} 
Table 5: Theoretical and actual average completion time (AC) obtained from LPT dispatching rule and LPT with EPO extension for samples of 5-day, 10-day, and 20-day periods. Best values are marked in bold. may differ in favor of one or the other rules (LPT and LPT with the EPO extension). However, for each of the analyzed objectives, the EPO extension allowed us to obtain more favorable results for the majority of samples. As in the previous case (\(C_{max}\)), the non-parametric Wilcoxon signed-rank test revealed statistically significant differences in the results.
# Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
## 2 System Model  
In order to deal with the problem of SSDF attack in the centralized collaborative spectrum sensing architecture, we propose a malicious cognitive user identification algorithm based on \(\beta\) function and feedback iterative method. Addressing the shortcomings of the prestige evaluation system under the traditional single channel condition and combining them with the characteristics of multiple sub-channels of collaborative sensing, we propose a prestige evaluation system based on multi-channel cooperation.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 3 Multi-Parameter Optical Analysis and Design of PM Scanning Heads
### Duty Cycle  
Using Equation (19), Table 1, a discussion on the duty cycle \(\eta\) can be made for the scanner design. Three cases can be considered:  
* \(\eta<1\) corresponds to the case where the lens diameter \(D\) (Figure 2) is positioned within the FOV defined by the \(\theta_{\min}\) and \(\theta_{\max}\) rotational angles in Figure 5a. Thus, the rays corresponding to the \(\theta_{1}\) and \(\theta_{2}\) angles (i.e., to the useful scanning domain) can be placed symmetrically to the O.A. and \[\theta_{min}<\theta_{1}<\theta_{2}<\theta_{max}\] (22)\(\eta=1\) is a difficult condition to apply. It is fulfilled for \[\theta_{min}=\theta_{1}\text{ and }\theta_{2}=\theta_{max}\] (23) and it means that the O.A. is no longer perpendicular to the incident ray, because the FOV is, in general, asymmetric with regard to the horizontal axis. From Figure 5a, using Equation (12), the particular case of the symmetry of the down-reflected and up-reflected rays with regard to the horizontal can be achieved for: \[\pi/2-2\theta_{min}=2\alpha{=}{>}\ x=(\sec\alpha)/\sqrt{2}\] (24) \(\eta>1\) corresponds to a lens with a diameter \(D\) larger than the linear FOV. This may be the case of a PM with a large \(n\). From Figure 6, for a small angle \(\alpha\), the angular FOV = \(4\alpha\) narrows--see, for example, Figure 6(a3). In practice, this case is applicable for double-pass PMs [1; 2], for which the incident beam is split in two. For such a configuration, the scan of the following PM facet begins before the scan on the current facet is completed. In this case \(\theta_{1}<\theta_{min}<\theta_{max}<\theta_{2}\).
# Building Penetration Losses at 3.5 GHz: Dependence on Polarization and Incidence Angle
## 2 Theoretical Background
### Uniform Plane Wave Incident on A Semi-Infinite Medium
Figure 2: Transmission losses, in dB: (**a**) with electric field perpendicular to the incidence plane; (**b**) with electric field parallel to the incidence plane. impinging on walls of the same material. For the case of perpendicular polarization, the attenuation increases monotonically with the incident angle. The attenuation for parallel polarization is lower and does not exhibit that large variation with the incidence angle.
## 3 Model-Updating with Automatic Mode Identification
### Model-Updating Approaches  
The model-updating method with automatic mode identification proposed in this paper comprises three modules: the modal-analysis module, the mode-identification module, and the model-updating module. In the modal-analysis module, the FE and experimental modal analyses are conducted separately to obtain the mode shapes and natural frequencies of the structure. In the mode-identification module, the similarity between the FE and experimental mode shapes is calculated using the ISR method, and the FE mode shape corresponding to the maximum ISR value is regarded as the same order mode as that of the experimental mode shape. The model-updating module, which includes the optimization algorithm and the parameterized FE model, is the key to driving the automatic execution of the model-updating process. A flow diagram of the model-updating method with automatic mode identification is illustrated in Figure 9.  
This study focuses on the impact of the boundary constraints on model-updating. To properly represent the boundary constraints, two different model-updating approaches are proposed and followed, as described in Table 5. The updating variables used in each approach are related to the boundary conditions, material properties, and structure dimensions.  
Figure 8: Auto-correlation of FE mode shapes based on different mode-identification indices (the closer the color is to red, the higher the similarity is, while the closer the color is to blue, the lower the similarity is). (**a**) Auto-correlation of FE mode shapes based on ISR and (**b**) Auto-correlation of FE mode shapes based on MAC.  
Figure 9: Flow diagram of the model-updating method with automatic mode identification.  
In the first approach, the boundary constraint is regarded as perfect rigid constraint; that is, the accuracy of model-updating is related only to the material properties and dimensions. In the second approach, an elastic support constraint is assumed, and the contact surface of the components is simplified to a number of springs in order to quantitatively characterize the support stiffness. In this paper, the plate was fixed to the exciter by a bolt, and the contact surface was the inner surface of the circular hole at the center of the plate. During model constraint simplification, the contact surface was discretized, and three springs in orthogonal directions were used on each node to simulate the elastic support between bolt and hole wall (Figure 10). The springs parallel to the surface of the plate provided support in the x- and y-directions. Since the support in these two directions is generated by the friction and extrusion between the components, \(k_{\rm H}\) was used to characterize the stiffness of the springs in these two directions. The springs on the vertical surface of the plate provided support in the z-direction. The support in this direction is mainly caused by the extrusion of the components; thus, \(k_{\rm V}\) was used to represent the stiffness of the z-direction springs.
# ARMONIA: A Unified Access and Metro Network Architecture
## 1 Introduction
NFV makes network functions available as software that can run in private data centers or in the public 'cloud'. These functions are deployed easily, quickly and economically [13]. In this context, open application programming interfaces (APIs) make the network dynamically programmable. In the optical access network, significant benefits arise from the virtualization of equipment in the telephone exchange (Central Office--CO), e.g., virtualization of the optical line termination (OLT) terminal via NFV and by scheduling operations via SDN [14]. SDN and NFV technologies, when combined with appropriate monitoring mechanisms, also enable the dynamic network operation. These mechanisms allow the network to continuously observe its state in terms of the physical layer and traffic conditions. Moreover, through SDN and NFV technologies, the different parts of the network (mobile, access, metropolitan) are integrated and jointly operated. This multiplies the benefits of the dynamic operation of each individual segment. Leveraging the aforementioned technologies, we propose the Self-Adjusting Unified Metropolitan and Access Network (ARMONIA). ARMONIA is able to monitor its condition, analyze it, and perform (almost real time) optimizations. The optical and IP equipment of the metropolitan and access networks are jointly controlled through a closed control loop operation, utilizing SDN and NFV technologies (Figure 1). In Section 3, we present some relevant algorithms that perform dynamic optimization. Moreover, in [15] we demonstrated a scenario where the network is optimized in near real time in the context of an athletic event that causes unexpected traffic spikes.
## 3 Results
### Evaluation of Sentinel-1A Soil Moisture
#### 3.1.2 S1A Soil Moisture vs. Microwave Products  
As the ground measurements were insufficient for spatial evaluation, a comparison with the coarse resolution products was conducted to examine the performance of the S1A soil moisture using S1A_0.25 maps. The first two columns in Figure 4 show the soil moisture of S1A_0.25, SMAP, SMOS, ASCAT, and AMSR2, as averaged in the dry and wet seasons, respectively. All of the products show higher soil moisture in the wet season, as expected. The SMAP and SMOS soil moisture show lower  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **S1A\_1K** & **S1A\_0.25** & **SMAP** & **SMOS** & **AMSR2** & **ASCAT** \\ \hline _ubRMSD_ & 0.053 & 0.018 & 0.038 & 0.029 & 0.052 & 0.047 \\ _MAD_ & 0.034 & 0.035 & 0.025 & 0.024 & 0.043 & 0.055 \\ \(R\) & 0.62 * & 0.78 * & 0.77 * & 0.79 * & 0.65 * & 0.41 * \\ N & 21 & 21 & 100 & 149 & 224 & 252 \\ \hline \hline \end{tabular}  
* Significant at \(p\) < 0.05.  
\end{table}
Table 2: The statistical metrics of the microwave soil moisture products vs. the in situ measurements.  
Figure 3: The total daily precipitation and temporal trends of the soil moisture measurements.  
values than the other products, especially in the dry season. While the soil moisture of ASCAT and AMSR2 appears to be homogeneous, the S1A_0.25 soil moisture shares a similar spatial pattern to SMAP and SMOS, in that the southeast is higher than the northwest in both the dry and wet seasons. In the dry season, the global products generally have soil moisture values of below 0.10 m\({}^{3}\)/m\({}^{3}\), while the southeast of S1A_0.25 reaches 0.15 m\({}^{3}\)/m\({}^{3}\). In the wet season, ASCAT and AMSR2 increase by about 0.10 m\({}^{3}\)/m\({}^{3}\) over the whole region. The increment of SMAP and SMOS is more obvious in the east, while S1A_0.25 shows a larger increment in the west side.  
The latter two columns in Figure 4 show the \(ubRMSD\) and \(R\) values of the microwave products, calculated with S1A_0.25 point by point. ASCAT, SMAP, and SMOS generally have \(ubRMSD\) values of under 0.06 m\({}^{3}\)/m\({}^{3}\), while AMSR2 shows higher values in the west. Among the products, SMAP obtains the lowest \(ubRMSD\), especially in the west, which shows the good temporal agreement with S1A_0.25. The Pearson's correlation coefficient (\(R\)) maps show similar results: the correlations between SMAP and S1A_0.25 are higher than 0.8 in most of the pixels, followed by SMOS, with half of the pixels showing high correlations. AMSR2 and ASCAT seem to be less correlated with S1A_0.25 as the \(R\) values generally range from 0.2 to 0.6. The results indicate that the S1A soil moisture has better agreements in both the spatial and temporal dimensions with SMAP and SMOS.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 3 System Model
### Network Model  
Suppose that a monitoring area is a two-dimensional plane \(A\), where \(A=m\times n\). There are \(N\) sensor nodes with different initial energy are randomly placed on the area \(A\). The sensing radius and communication radius of each sensor node are \(R_{s}\) and \(R_{c}\), respectively. The coordinate of the sensor node \(s_{j}\) is denoted by \((x_{j},y_{j}),1\leq j\leq N\), which is assumed to be known by the sink. The sensor node set is \(S=\{s_{1},s_{2},...,s_{N}\}\). The set of MTPs is \(P=\{p_{1},p_{2},...,p_{M}\}\), where \(p_{k}\) is the \(k\)-th MTP located at \((x^{\prime}_{k},y^{\prime}_{k}),k\in[1,M]\), and \(M\) is the number of MTPs. These MTPs are distributed in the area \(A\). In this paper, we assume that the sensor network is static. To guarantee that all MTPs be covered, \(N\) and \(M\) satisfy \(N\gg M\).  
In order to ensure all the collected data be sent to the sink, the network must be fully connected. Besides, all the channels among all sensor nodes and the sink are reliable. That is, there is no transmission error and retransmissions. In [23], the author has proved the necessary condition for the network connectivity is that, if the communication radius \(R_{c}\) of sensor nodes is not less than twice the perception radius \(R_{s}\), the network can achieve full connectivity under the premise of full coverage. Let \(R_{c}\)=2\(R_{s}\). Subsequently, as long as the network is fully covered, it can be assumed to be connected.  
A binary variable \(G_{j,k}\) is used to indicate whether the sensor node \(s_{j}\) can cover the MTP \(p_{k}\), which can be defined as  
\[G_{j,k}=\{\begin{array}{ll}1&if\sqrt{(x_{j}-x^{\prime}_{k})^{2}+(y_{j}-y^{ \prime}_{k})^{2}}\leq R_{s},\\ 0&otherwise.\end{array} \tag{1}\]In (1), \(\sqrt{(x_{j}-x_{k}^{\prime})^{2}+(y_{j}-y_{k}^{\prime})^{2}}\) is the distance between the MTP \(p_{k}\) and the sensor node \(s_{j}\). If the distance is smaller than \(R_{s}\), \(G_{j,k}=1\) indicates that MTP \(p_{k}\) can be covered by sensor node \(s_{j}\). Because sensor nodes are distributed randomly, the MTP may be covered by \(v\) sensor nodes at the same time, this phenomenon can be expressed as  
\[G_{1,k}+G_{2,k}+...+G_{v,k}=v. \tag{2}\]  
It can be seen from (2) that, if MTP \(p_{k}\) is covered, the following condition must be satisfied.  
\[\sum_{j=1}^{N}G_{j,k}\geq 1. \tag{3}\]
# Graphical and Analytical Quantitative Comparison
## 4 The Case Study of San Francesco di Paola
### Application  
#### 4.3.1 MTLM and TLA  
Using MTLM, we obtain a horizontal thrust in the upper part of the dome \(Px_{75}=170.0\) kN, which increases for the first five voussoirs then stabilizes at a value of 322.5 kN (see Table 1). This is due to the fact that these first five voussoirs absorb the lantern load, showing a significant hoop forces value that, from the sixth voussoirs onwards, is almost zero. The variation in the thrust component \(Px_{i}\) is clearly shown in the force polygon in Figure 8.  
The classical method of TLA can be derived as a particular case of the MTLM, by imposing a vanishing behavior of the hoop forces along each meridian. The most evident consequence in terms of thrust is that the thrust itself is constant from the top to the bottom of the dome.  
It is worth noting that, with classical TLA and MTLM, it is not possible to obtain the same profile for the thrust line. Among the infinite set of thrust lines that can be obtained using the slicing technique, we choose the one which returns as similar a profile as possible to the one selected with the MTLM by fixing the points of passage of the thrust line in the upper and lower part of the dome and considering a horizontal thrust value \(Px_{75}=Px_{1}=323.7\) kN (see Figure 9).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{P_{x75}}\) & \(\mathbf{P_{y75}}\) & \(\mathbf{P_{x1}}\) & \(\mathbf{P_{y1}}\) & \(\mathbf{H_{175}}\) & \(\mathbf{H_{11}}\) \\ \hline MTLM & 170.0 & 54.9 & 322.5 & 628.8 & 867.2 & 0.16 \\ TLA & 323.7 & 54.9 & 323.7 & 628.8 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data comparison between TLA and MTLM in terms of thrusts and hoop forces in the upper part of the dome (voussoir 75) and in the bottom part of the dome (voussoir 1). All values are reported in kN.  
Figure 8: Equilibrium of the dome of San Francesco di Paola with MTLM. In the dome section (top left), the thrust line is drawn in red and the vectors representing the weight of each voussoir are drawn in blue. In the force polygon (on the right), the horizontal thrust is outlined in green and the vertical thrust in blue. Bottom-plan view of the lune with hoop forces (bottom left) plotted in green; the hoop forces’ distribution is shown in orange.  
The result of the comparison of these two methods is that the vertical component of the thrust is the same, due to the considered loads; additionally, the vertical component of the thrust in the lower part has an almost similar value. However, the horizontal component in the upper part of the dome through the TLA is significantly lower than the one obtained with the MTLM, since there is a strong contribution of the hoop forces in this part that is not considered by the classical TLA (see Table 1).