esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Western TMVB
The shape of the HVSR curve corresponding to this station could be attributed to variations in the topography or a deep interface with Oligocene formations. SNID (Figure 4) and MA48 (Figure 5) were deployed on Quaternary sedimentary rocks. The amplitudes in SNID could be associated to lateral heterogeneities by means of its location near Neogene formations. Likewise, some of the multiple peaks in MA48 could be spurious or related to multiple layers of sediments at variable depth. Figure 4: Average HVSR curves for stations located in the western sector of the TMVB. Blackline: HVSR results using ambient noise records; redline: HVSR results from earthquake records. In the remaining stations, clear peaks of different amplitude can be observed, revealing different impedance contrasts between sediment layers and the bedrock: BAVA, JANU, MA44, MA45, ANIG, MA40, MA41, PAVE, GARC, ALPI, SANM (Figure 4), CDGZ, COLM, CANO, and ESPN (Figure 5). This is also indicative of possible amplifications in ground motion during the occurrence of seismic events. In the case of stations BAVA and JANU, located on Oligocene felsic rocks, the observed peaks at low frequencies could be associated to thick sedimentary deposits. High clear peaks at 3 and 4 Hz are observed at stations MA44 and MA45. These may be produced by strong impedance contrasts with interlayered sedimentary and volcanic rocks from the Lower Cretaceous. The stations located on mafic and intermediate rocks from the Neogene and Quaternary have a heterogeneous response (ANIG, MA40, MA41, PAVE, GARC, ALPI, and SANM) (Figure 4). In general, a clear peak is observed above 5 Hz, which could be attributed to impedance contrast with thin layers of sediment. It should be noted that the average HVSR curve obtained with earthquake data at ANIG station does not vary significantly in its shape to that obtained from ambient noise. There is a peak above 10 Hz that could have been produced by the presence of thin volcanic layers. Regarding the average HVSR curves of stations on Quaternary sedimentary rocks (ESPN, CDGZ, COLM, and CANO) there are varied shapes (Figure 5). In ESPN, there is a peak above 10 Hz which may require the analysis of a wider frequency bandwidth. The clear peak in CANO could be associated to contact with Neogene and Quaternary rocks. As for CDGZ, there is a high peak near 1 Hz, attributable to impedance contrast with layers of the Lower Cretaceous or thick layers of sediments. Likewise, in COLM there is a small peak around 10 Hz, probably due to the presence of shallow unconsolidated sediments.
###### Abstract  
This article presents a generic flexible framework for an End-to-end Instrument Performance Simulation System (EIPS) for satellite atmospheric remote sensing instruments. A systematic process for developing an end-to-end simulation system based on Rodgers' atmospheric observing system design process has been visualised. The EIPS has been developed to support the quantitative evaluation of new satellite instrument concepts in terms of performance simulations, design optimisation, and trade-off analysis. Important features of this framework include: fast radiative transfer simulation capabilities (fast computation and line-by-line like simulations), applicability across the whole electromagnetic (EM) spectrum and a number of integrated retrieval diagnostics. Because of its applicability across the whole EM spectrum, the framework can be usefully applied to synergistic atmospheric retrieval studies. The framework is continually developing and evolving, and finding applications to support and evaluate emerging instrument and mission concepts. To demonstrate the framework's flexibility in relation to advanced sensor technologies in the microwave range, a novel superconducting transition edge sensor (TES) -based multi-spectral microwave instrument has been presented as an example. As a case study, the performance of existing multi-spectral-type microwave instruments and a TES-technology-based multi-spectral microwave instrument has been simulated and compared using the developed end-to-end simulation framework.  
End-to-end simulators; satellite atmospheric remote sensing; performance simulations; trade-off analysis; design optimisation; information and retrieval error analysis; microwave +
Footnote †: journal: Remote Sensing, 2019, 11, 1412; doi:10.3390/rs11121412
# Headspace Volatiles and Endogenous Extracts of _Prunus mume_ Cultivars with Different Aroma Types
## 2 Results  
Identification and Relative Content Analysis of the Composition of the P. mume Cultivars with Four Aroma Types  
The components of eight _P. mume_ cultivars from strong-scented, fresh-scented, sweet-scented, and light-scented types were analyzed by headspace solid-phase microextraction/gas chromatography-mass spectrometry. A total of 66 headspace volatiles were putatively detected (Figure 1a), and their relative contents are listed in Table S1. There were 57, 61, 62, and 56 headspace volatiles corresponding to the strong-scented, fresh-scented, sweet-scented, and light-scented types, respectively. The headspace volatiles included 33 phenylpropanoids/benzenoids, 25 fatty acid derivatives, and 9 terpenoids. The relative contents of various aroma substances in _P. mume_ cultivars are shown in Figure 2a. Among them, the relative content of phenylpropanoids/benzenoids was the highest one, which ranged from 88.36 to 96.81%. The relative content of fatty acid derivatives varied from 2.24 to 14.12%, and the content of terpenids was the lowest, with the relative amount ranging from 0.65 to 3.92%.  
Figure 1: Heatmap and dendrogram analysis of compounds of eight _P. mume_ cultivars with four aroma types. (**a**) headspace volatiles; (**b**) endogenous extracts. The color of the heatmap ranges from dark blue (value, \(-2\)) to red (value, \(2\)) in the natural logarithmic scale. Data are presented with means of biological replicates. The values were normalized by log\({}_{10}\) transformation. ’DEZS’ and ’JM’ belong to the fresh-scented _P. mume_ cultivars; ’MDL’ and ’DFH’ belong to the lighted-scented _P. mume_ cultivars; ’FPCZ’ and ’JN’ belong to the strong-scented _P. mume_ cultivars; ’XLE’ and ’LY’ belong to the sweet-scented _P. mume_ cultivars.  
Figure 2: The relative contents of various aroma substances in _P. mume_ cultivars with four aroma types: (**a**) headspace volatiles; (**b**) endogenous extracts.  
A total of 74 endogenous extracts (Figure 1b), including 24 phenylpropanoids/benzenoids, 43 fatty acid derivatives, and 7 terpenoids (Table S2) tentatively, were identified by organic solvent extraction combined with GC-MS. Among them, 68, 65, 70, and 66 endogenous extracts were detected in strong-scented, fresh-scented, sweet-scented, and light-scented types, respectively. As shown in Figure 2b, the phenylpropanoids/benzenoids were the ingredient with the highest percentage in all the aroma types and varied from 84.77% to 89.73%. Similarly, the relative content of fatty acid derivatives ranged from 7.50% to 10.08%, and the relative content of terpenoids accounted for 2.33% to 5.15%.
_Appl. Sci._**2011**, \(1\), 1-11; doi:10.3390/app1010001  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Objective Measures of Emotion During Virtual Walks through Urban Environments**  
**[PERSON] 1 and [PERSON] 1,2,3,***  
1 Biological Psychology Unit, Faculty of Psychology, University of Vienna, Liebiggasse 5, A-1010, Vienna, Austria; E-Mail: [EMAIL_ADDRESS]  
2 School of Psychology, Faculty of Science and Information Technology, University of Newcastle, Newcastle, 2308 NSW, Australia  
3 Applied Neuroscience, Neuroconsult, Guntergasse 3/3, A-10190 Vienna, Austria  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +61-2-492-172-60.  
_Received: 4 May 2011; in revised form: 1 June 2011 / Accepted: 1 June 2011 / Published: 1 July 2011_  
**Abstract:** Previous studies were able to demonstrate different verbally stated affective responses to environments. In the present study we used objective measures of emotion. We examined startle reflex modulation as well as changes in heart rate and skin conductance while subjects virtually walked through six different areas of urban Paris using the StreetView tool of Google maps. Unknown to the subjects, these areas were selected based on their median real estate prices. First, we found that price highly correlated with subjective rating of pleasantness. In addition, relative startle amplitude differed significantly between the area with lowest versus highest median real estate price while no differences in heart rate and skin conductance were found across conditions. We conclude that interaction with environmental scenes does elicit emotional responses which can be objectively measured and quantified. Environments activate motivational and emotional brain circuits, which is in line with the notion of an evolutionary developed system of environmental preference. Results are discussed in the frame of environmental psychology and aesthetics.  
**Keywords:** startle reflex modulation; urban environments; emotion and motivation; affective appraisal; environmental preference
## 2 RTD-A Control Scheme
### Stability Analysis  
#### 2.1.1 State Variable Form  
When the RTD-A controller based on the nominal model \(G(s)\) is implemented on the plant with model uncertainty (\(\lambda\)), the resulting closed-loop behavior may be represented in the discrete state-space form in (14) and (15) [13].  
\[X(k+1)=RX(k)+Qu(k)+G_{s}y_{d}+G_{i}d+G_{o}d_{o}, \tag{14}\]  
\[y(k)=\Big{[}\begin{array}{cccc}0&1&0&0&0\end{array}\Big{]}X(k) \tag{15}\]  
where \(X(k)\), \(y_{d}\), \(d\) and \(d_{o}\) are the state vector, setpoint, load disturbance and output disturbance respectively.  
\[X(k)=\Big{[}\begin{array}{cccc}y^{*}(k+1)&y(k+1)&\hat{g}(k+1)&e_{D}(k)&\mu( k+1)\end{array}\Big{]}^{T}.\]  
The control action \(u(k)\) is given by (16):  
\[u(k)=SX(k)+By_{d}. \tag{16}\]  
The characteristic equation of the closed loop system is given by (17):  
\[|zI-R-QS|=0  
\[D=-\sum\limits_{i=1}^{N}\eta_{i}(1+\beta(i));\beta(i)=\frac{\theta_{D}}{1- \theta_{D}}(1-(1-\theta_{D})^{m+i}),\]  
\[E=\sum\limits_{i=1}^{N}\eta_{i}\beta(i),F=-\sum\limits_{i=1}^{N}\eta_{i}a^{i-1} b,\zeta=\frac{1}{b\sum\limits_{i=1}^{N}\eta_{i}^{2}}.\]
## 3 Empirical Gramians
### Parameter-Space Empirical Gramians
#### 3.2.3 Empirical Cross-Identifiability Gramian  
If a system is square, the augmented system (5) remains square and for linear systems symmetry is also preserved. Hence, a cross Gramian of the augmented system is computable [11].  
**Definition 8** (Empirical Joint Gramian).: _The **empirical joint Gramian** is given by the empirical cross Gramian of the augmented system._  
The joint Gramian is an augmented cross Gramian and has a similar block structure as the augmented observability Gramian,  
\[W_{I}=\begin{pmatrix}W_{X}&W_{m}\\ 0&0\end{pmatrix},\]  
but due to the uncontrollable parameter-states, the lower (parameter-related) blocks are identically zero. Nonetheless, the observability-based parameter identifiability information can be extracted from the mixed block \(W_{m}\).  
**Definition 9** (Empirical Cross-Identifiability Gramian).: _The **empirical cross-identifiability Gramian** is given by the Schur-complement of the symmetric part of the empirical joint Gramian for the lower right block:_  
\[W_{I}=0-\frac{1}{2}W_{m}^{\intercal}(W_{X}+W_{X}^{\intercal})^{-1}W_{m}.\]  
Thus, the empirical joint Gramian enables the combined state and parameter analysis by the empirical cross Gramian \(W_{X}\) and empirical cross-identifiability Gramian \(W_{I}\) from a single \(N\times N+P\) matrix. Note that the empirical joint Gramian may also be computed based on the non-symmetric cross Gramian. Additionally, a parameter Gramian, such as \(W_{I}\) or the \(W_{I}\), could be balanced with the loadability Gramian from [15].
# Parameter Optimization for Ship Antiroll Gyros
## 5 Comparative Analysis of Parameter Optimization Results  
### Principles of PSOA and BFOA  
PSOA is a direct search method, which only uses the function value instead of the derivative, so it is very effective in solving the optimization problem of functions that are not differentiable or difficult to differentiate. As shown in Figure 10, PSOA searches a set of points around the current point, looking for one where the value of the objective function is lower than the value at the current point.  
The equation of roll reduction rate is nonlinear, which is difficult to solve using the gradient-based optimization algorithm. So, PSOA is a feasible method to achieve the optimal object of this paper. However, PSOA tends to fall into the local optimal solution, and the selection of initial value has a great impact on the result. To overcome these shortcomings, we further applied a parallel search method, BFOA.  
BFOA is a new swarm intelligence optimization algorithm, which can be summarized as searching for food, moving the location, and digesting food. As shown in Figure 11, BFOA achieves optimization through three behaviors: Chemotaxis, replication, and dispersal. The chemotaxis can ensure the local search ability of bacteria, the replication can accelerate the search speed of bacteria, and the dispersal can enhance the global optimization ability of the algorithm.
# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
## 4 Experimental Results and Comparative Performances  
### Pollution Episode Segmentation  
The comparative performances of the proposed episode segmentation algorithms in terms of the three measures are tabulated in Table 1. It is seen that RS and SB have more balanced performance on precision and recall than HD and ED. This is a desired property because false positives from cuts will result in many short shots without delivering meaningful episodes, which hinder the explanation of complete pollution cycles. On the other hand, RS and HD are overall better performers whose F1-Score is above 73%, followed by SB, which obtains an F1-Score of 68%. The worst performer among the four competing methods is ED, whose F1-Score is 62%. The reason for the performance ranking may be due to the low resolution and shallow color depth of the air-pollution alert map, which is resolved as a 10 \(\times\) 10 image with only 10 pollution-alert color categories. The edge information and moment statistics of such an image are not sufficient for effectively differentiating the difference between cut and non-cut frames. Another disadvantage of ED is that the edge responses are relative values rather than absolute ones, and it cannot tell the difference in the edge between category \(1\) and category \(2\) or between category \(9\) and category \(10\), explaining why ED is the worst performer.  
In Figure 4, we show the detected cuts and the true labeled cuts on an example time sequence by using the proposed episode segmentation methods. The true labeled cuts are marked with a red border, while the cut determined by the applied method is marked with a black border. When the true labeled cut is correctly detected by the applied method, the cut is marked with both red and black borders. It is observed that there are nine true labeled cuts out of \(80\) frames in the time sequence. The nine true labeled cuts belong to three soft transitions, which segment the time series into four meaningful pollution episodes. The RS, HD, ED, and SB detect \(5\), \(6\), \(14\), and \(13\) cuts, respectively. This reveals that ED and SB may tend to produce more short episodes. A promising phenomenon is that the three soft transitions are detected by all methods, with at least one cut-frame detected within each soft transition. ED and SB may produce more cuts than the true labeled cuts. However, if we examine these cuts closely, the true cuts labeled by the experts are intended to segment the complete pollution cycle consisting of concentration accumulation, transportation, and diminishing. While the cuts detected by ED and SB may produce shorter episodes such as concentration accumulation or pollution diminishing.
# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 5 Result
### The Location Result of the Area without Fingerprints Collection  
The results of the positioning data of the experiment are shown in Table 4. Figure 14 is the location test result of test area 2, and the root mean square location error is 1.1237 m. The results show that the rough positioning can be completed by simply relying on the fingerprint positioning data generated by the simulation calculation, and it can also meet the need of low-precision positioning without manual fingerprints collection.  
Figure 12: Comparison of positioning errors of different positioning algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **KNN** & **SVM** & **[PERSON] [10]** & **[PERSON] [13]** & **Our Method** \\ \hline RMS error (m) & 1.1821 & 1.0696 & 0.7714 & 0.9253 & 0.4850 \\
95\% error (m) & 2.2727 & 1.8424 & 1.6194 & 1.4727 & 1.026 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Location Accuracy of different fingerprint positioning algorithms.  
Figure 13: Cumulative Distribution Function of errors from different positioning algorithms.
## 3 Methodology
### SegFormer Network Model  
The SegFormer network model is a simple and efficient semantic segmentation model recently proposed in the field of image segmentation, using the same encoder-decoder structure as the traditional segmentation model [22]. Unlike traditional convolutional neural network-based segmentation models, the encoder of the SegFormer network model adopts the prevailing Transformer structure. The encoder body consists of four stages, each made of a series of Overlap Patch Embeddings (OPE) layers, Efficient Multihead Self-Attention (EMSA) layers, and Mix Feed Forward (MFFN) layers [27]. The overlap patch embeddings layer is mainly used to reduce the spatial resolution of the front feature map and convert the two-dimensional information in the feature map space into the one-dimensional features required by the Efficient Multihead Self-Attention layer. The latter computes the high-dimensional representation of these features via self-attentiveness, while the final Mix Feed Forward layer is mainly used to enhance the expression of the features. Because the Transformer-based encoder has a larger perceptual field than the traditional convolutional neural network-based encoder, the decoder of the SegFormer network model consists of only a series of Multilayer Perceptron (MLP), and its workflow is divided into four steps. First, the channels of multi-level feature information output from the encoder are adjusted to the same dimensionality via an MLP layer. Then, a linear interpolation algorithm is used to uniformly upsample the spatial dimension of the multi-level feature information to one-quarter of the original image size and stitch it into the channel dimension. In the third step, the stitched features are then fused with information using one more MLP layer. In the final step, the fused features are used for the prediction of the final segmentation mask via another MLP. The structure of the SegFormer network model is shown in Figure 1.
## 3 Transmission Characteristics of Ultra-Bend-Resistant 4-Core SXC
### Transmission Loss  
In this section, we will show the new finding of transmission loss after the cabling process on the 4-core SXC. Firstly, we measured the mode field diameters (MFD) of these four cores which are approximately 8.4 \(\upmu\)m at 1310 nm. And the measured values of cutoff wavelength (from 1\(\upmu\) to 4\(\upmu\)) are 1207.8 nm, 1213.8 nm, 1228.9 nm and 1216.5 nm, respectively. The transmission loss of each channel in the O-band was measured by the prepared tapered-type FIFO device and optical time domain reflectometry (OTDR), which are shown in Figure 4a. The duration time of probe pulse and average time for OTDR were adjusted to 100 ns and 30 s, respectively. In our experiment, the maximal insertion loss and average XT between adjacent channels of the FIFO device are, respectively, less than 0.8 dB and \(-\)70 dB at 1310 nm, which can achieve a high coupling efficiency with the fabricated 4-core SXC [20]. By using the fusion splicer for the core alignment of the rotational direction and controlling the cleaved angle (\(<\)0.5\({}^{\circ}\)), the fusion splice loss between the tail fiber of the FIFO device and the 4-core fiber/SXC can be less than 0.2 dB. The 2.9 km long standard single-mode fiber (SMF) was firstly placed to remove the blind zone of OTDR. Figure 4b exhibits the transmission loss results of the 4-core fiber and 4-core SXC, respectively. It can be found that the transmission loss of all cores before cabling is less than 0.500 dB/km at 1310 nm. It is analyzed that the transmission loss of our prepared 4-core fiber can be mainly divided into four parts: the intrinsic infrared absorption of silica (SiO\({}_{2}\)) material and residual OH- (\(\alpha_{l}\)), the Rayleigh scattering caused by microscopic density fluctuation (\(\alpha_{R}\)), the micro-bending loss induced by small-scale bends from fiber coating (\(\alpha_{b}\)) and the potential coating-leakage loss (\(\alpha_{l}\)). In the future, by adjusting the GeO\({}_{2}\) dopant, further reducing the OH- content and optimizing the process of fiber coating, the intrinsic transmission loss of the 4-core fiber might be below 0.37 dB/km in the O-band [1]. Moreover, there are no significant changes in transmission loss of the 1\(\upmu\), 2\(\upmu\) and 3\(\upmu\) core after cabling, as shown in Figure 4. However, the transmission loss of the 4\(\upmu\) core shows an obvious degradation of 0.105 dB/km due to the cabling. This can be explained by the fact that the 4\(\upmu\) core which is the nearest neighbor to the marker core would suffer most from the geometric deformation of the core and the longitudinal structural fluctuation when the 4-core fiber is cabled. It would cause the micro-bending loss due to fiber compression in the longitudinal direction of the 4-core SXC [3; 16; 17]. Fortunately, finer control over a tight construction or adjusting the buffer coating material can be used to minimize this cabled loss degradation (\(<\)0.010 dB/km) [1; 21].  
Figure 4: Transmission loss test. (**a**) Experimental setup by OTDR and (**b**) test results.