esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Correlations of VOCs  
The Spearman correlation test was performed to understand the relationships between the identified VOCs. In Figure 5, the significant correlations (\(p<0.05\)) of VOCs are presented in blue (positive correlation) and red (negative correlation) respectively, and non-significant correlations are presented in white. The VOCs showed significant positive correlations within their respective chemical groups, which are (a) alcohols/aldehydes/esters (GLVs related VOCs), (b) terpenes, and (c) ITCs. It was noted that the GLVs related VOCs had significant negative correlations with several sulphur compounds (e.g., dimethyl disulphide and dimethyl trisulfide) and showed significant positive correlations with nitriles, but had no significant correlations with most of terpenes and ITCs. Further, terpenes and ITCs did not appear to show any correlation.  
The significant correlations of VOCs with correlation coefficients (r values) larger than \(0.8\) for positive correlations and lower than \(-0.5\) for negative correlations are highlighted in a network plot (Figure S2). It was obvious that VOCs from the same pathway, such as GLVs from the fatty acid pathway (LOX pathway), terpenes from the MEP pathway, and ITCs from the GLS pathway, had highly positive  
Figure 4: Metabolic pathway differences in _Brassica oleracea var. capitata_, _Brassica oleraca var. oleraca_, _Raphanus sativus_ and _Brassica rapa subsp. Pekinensis_ based on comparative genomics. The Venn diagram shows the similarity and differences between metabolic pathways among the four plant species: _Brassica oleraca var. capitata_ (in orange), _Brassica oleracea var. oleraca_ (in green), _Brassica rapa subsp. Pekinensis_ (in blue, using PlantCyc databases) and _Raphanus sativus_ (in violet, using RadishBase). The numbers in these Venn diagram represent the numbers of shared and unique metabolic pathways and sub-pathways in these species.  
correlations. In contrast, the VOCs from different pathways either had no correlations or showed negative correlations (e.g., dimethyl disulphide, dimethyl trisulfide, and 3-butenyl isothiocyanate vs. GLVs).
# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## 3 Analyses and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Relation** & ** NDVI-VH** & ** NDVI-VV** & ** LST-VH** & ** LST-VV** & **VH-VV** & **NDVI-LST** \\ \hline
**Classes** & \multicolumn{5}{c}{**Correlation Coefficient/Statistical Significance**} \\ \hline Swamp & 0.64/0.03 & 0.00/0.99 & 0.58/0.05 & 0.02/0.00 & 0.69/0.01 & 0.88/0.00 \\ Water & 0.74/0.01 & 0.21/0.53 & 0.77/0.01 & 0.23/0.49 & 0.67/0.02 & 0.87/0.00 \\ Bog & 0.79/0.00 & 0.48/0.14 & 0.70/0.02 & 0.54/0.09 & 0.60/0.04 & 0.94/0.00 \\ Land & 0.81/0.00 & 0.40/0.92 & 0.72/0.01 & 0.08/0.82 & 0.34/0.28 & 0.93/0.00 \\ Wetland Mixture & 0.78/0.00 & \(-0.19/0.57\) & 0.75/0.01 & 0.00/1.00 & 0.19/0.58 & 0.94/0.00 \\ Sediment Bog & 0.30/0.37 & \(-0.34/0.30\) & 0.30/0.36 & \(-0.26/0.44\) & 0.32/0.31 & 0.93/0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Monthly Correlation between the average values of LST, NDVI, and SAR data. can be obtained with a combination of the data. In the analyses of the backscatter signature in [43], the maximum values occur when soil moisture is high and vegetation is fully developed with values of approximately \(-5\) dB in the HH polarization, while the minimum occurs in the leaf-off season with values of approximately \(-20\) dB. The results of the VH polarization in our study showed similar results, where the minimum value is \(-20.15\) dB and the maximum \(-14.22\) dB. Regarding the LST data, similar to the findings in [PERSON] et al. [21], this study also indicates that the land surface temperature of wetlands is substantially affected by the air temperature. In our study, we have confirmed this statement by finding the correlation between the average monthly air temperatures and land surface temperature, which is \(0.9\). Observing the results in Figures 7 and 8, as well as the statistical results in Figure 9, beside their different characteristics, the spectral signature of the Land, Wetland Mix, Bog, and Sedimentary Bog are similar, and very unstable except for the Sedimentary Bog class in the leaf-off season. This corresponds to the low spatial resolution of the used remote sensing images because the majority of the pixels represents a mixture of several land/wetland cover types [44].
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 4 Results
### Comparison to Prior Arts  
To thoroughly validate the performance of our proposed local video editing method, we selected several state-of-the-art methods for comparison. Specifically, we selected AnyV2V [41], StableVideo [37], and MotionDirector [45] as competitors, as they represent prominent approaches in video editing. AnyV2V: Known for its versatility in video-to-video translation tasks, this method is centered around maintaining temporal consistency across frames. StableVideo: A method that places emphasis on stability during video editing, particularly for long-term temporal coherence. MotionDirector: Renowned for dealing with complex motion edits, it focuses on modifying motion trajectories while preserving realism in spatial domains. Each of these competing methods has its own unique strengths in the realm of video editing. These methods were chosen because they address the challenges of local edits and texture transfer. Their focus closely aligns with our objectives. By comparing with these approaches, we can showcase the distinct advantages of our method in handling fine-grained edits and maintaining temporal coherence.
## 4 Examples of Critical Constructability Features in Facade Panels of the Orchard Commons Project
### Feature 3: Vertical Shift Between Panel Bays  
**Constructability Constraints and Experts' Knowledge:** In the OC project, the ideal constructability scenario would involve repeating the panel bays (vertical stripes) at certain horizontal intervals along the building facade. Nonetheless, given the design intent was to evoke a sense of diversity and uniqueness through the facade design, mere repetition would not capture the unique and intricate essence from an architectural standpoint. Simultaneously, from a constructability viewpoint, adhering to a specific sequence in panel positioning would emerge as a pragmatic constraint in contrast to a random placement. Consequently, the OC project's designers opted to integrate a constructability constraint for the vertical alignment of the panel bays. This allowed them to induce a vertical displacement from one bay to the next, fostering more variety in the facade pattern and embodying the notion of subtle variation.  
Figure 5: Amplitude study using straight panel edges showcasing different design choices. Each image represents different edge inclinations. The red dot is a slider that enables creating a new variation of the design by changing the edge inclinations.  
Upon consulting with practitioners, the designers settled on vertical displacements ranging from 3 to 7 floors between adjacent panel bays. Moreover, for constructability reasons, they ensured each displacement was a whole number of floors, ruling out shifts like 3.5 floors. By embracing such constraints, each panel bay mirrored the panel sequence of other bays, with the sole distinction being the commencement with a different panel type. Figure 6 displays the positioning of panels across different bays and the vertical displacement between them, which can be identified via diverse color shades and corresponding numbers.  
**Constructability Feature and Computational Implementation:** The highlighted constructability feature here serves as a paradigm for features addressing constraints tied to the \"relational placement\" between two or multiple components. In numerous architectural concepts, exceptional patterns emerge by arraying similar components in unique manners, achievable through either component shifting or rotation. The relational placement attribute profoundly affects the installation and synchronization of components, as minor errors would manifest in the overarching facade pattern. In computational design, relational component positioning can be articulated through a function that relocates each component based on its relative position and orientation to an adjacent component (\(f(p_{\mathit{(neighhor)}},\Delta\,d,\Delta\,\alpha)\)).  
Figure 6: Vertical displacement between panel bays.  
Another noteworthy constructability feature in this context pertained to the \"Max. and min. horizontal distance between panels\" in this study, which was aligned with the relational placement constraints.
## 2 Remarks on the Topology of Microgrid
### Microgrid Applications
Optimizing the sizing and operation of microgrids in various applications requires considering the characteristics of each environment and sector, along with the relevant indicators for energy source selection. Further analysis of these factors, including optimal resource use, system resilience, and constraints tailored to different operating conditions, refines energy planning. Grid-interconnected microgrids, for example, improve system resilience and flexibility with star, ring, or mesh configurations, enabling power sharing and improving the reliability of critical loads [38,39]. In remote or resource-constrained environments, measures such as \"mass per unit load\" (MPUL) provide an indicator of efficiency by assessing system mass relative to power demand, making this measure applicable in regions with high transportation and installation costs [40]. Adequate microgrid protection relies on adaptive strategies, such as directional overcurrent relays and coordinated algorithms, to maintain stability amid fluctuating loads and bidirectional power flows [38]. Advanced battery degradation models further aid in selecting durable energy storage options and optimizing system lifetime under harsh conditions [40]. This planning approach, which integrates adaptive architectures, efficient resource use, and robust protection strategies, improves the performance of microgrids in various application scenarios.
## 3 Results
### Alignment of TEM and SIMS Images Using MoDaT  
The sample presented in Figure 2 was also analyzed via TEM (Figure 3A). We distinguished corneocytes and NP aggregates. To overlay the TEM and SIMS images acquired from the same areas of the sample (Figure 3), it was necessary to account for the differences in size and resolution: the original TEM images were 672 \(\times\) 518 pixels, while the original SIMS images were 256 \(\times\) 256 pixels. Note that here, Figure 3A is a mosaic of two TEM images in order to represent the same field of view as the SIMS images. In addition, the corneocytes do not have the same orientation.  
The images required alignment, and for this purpose, we developed a module within the MoDaT software. This module enables SIMS images to be aligned with the TEM image by applying anisotropic transformations. To perform these transformations, landmarks were placed on the images to be aligned (Figure 3B,C,E,F). These landmarks created a mesh that corresponded between the TEM and SIMS images (Figure 3B,C). Figure 3D shows the overlay of the TEM image with the SIMS image of \({}^{12}\)C\({}^{14}\)N. The alignment appeared satisfactory, with corneocytes well superimposed and only a slight overflow of the \({}^{12}\)C\({}^{14}\)N signal (magenta) onto the uranium contrast (gray). This alignment could then be applied to other SIMS images. By extending the alignment mesh (Figure 3F compared with Figure 3E), we could consider both the corneocytes and the NPs aggregates. Figure 3G,H is the overlay of the TEM image with the \({}^{12}\)C\({}^{14}\)N and \({}^{16}\)O images (Figure 3G) and with the \({}^{12}\)C\({}^{14}\)N and \({}^{48}\)Ti\({}^{16}\)O images (Figure 3H). The aggregates, as observed at masses \({}^{16}\)O and \({}^{48}\)Ti\({}^{16}\)O, are well centered with those seen in the TEM image, though they do exhibit a slight overflow. This suggests that the alignment was satisfactory despite the differences in the original images and the physics of the instruments. As the \({}^{16}\)O and \({}^{48}\)Ti\({}^{16}\)O images seem to reveal the same structures, potentially NPs aggregates, we checked whether the two images were correlated. We performed a Pearson test to compare the images. The correlation coefficient was 0.856 with a \(p\)-value of 2.2 10\({}^{-16}\), which meant that there was a strong correlation between the two images and that the TiO\({}_{2}\) NPs could be localized using the \({}^{16}\)O signal and/or the \({}^{48}\)Ti\({}^{16}\)O signal. However, in Figure 3E,F, there is a slight \({}^{16}\)O signal coming from the corneocytes. So, to avoid any confusion between the \({}^{16}\)O signal from the NPs and that from the corneocytes, it was preferable to focus on the specific signal to the NPs, i.e., \({}^{48}\)Ti\({}^{16}\)O (Figure 3H). All these observations show that the aggregatesidentified via TEM and SIMS at masses of \({}^{16}\)O and \({}^{48}\)Ti\({}^{16}\)O were located on the outside of the corneocytes. They did not appear to have crossed the corneocyte barrier.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 3 Methodology
### Principal Component Analysis (PCA)  
To reduce the volume of the data prior to the application of any classification method, a data compression is necessary to be applied especially when a huge amount of data is involved in the study. This data compression reduces the dimensionality of the classifier workspace and eliminates redundancies and nonessential information in raw data that may lead to inaccurate classification. Among data compression techniques, the principal component analysis is a statistical compression technique which converts high dimensional data to low dimensional data by selecting the most important features capturing maximum information about the dataset [49; 50]. Data compression by means of PCA is accomplished by projecting each sample block of data along the directions of the individual orthonormal eigenvectors of the covariance matrix of the data. The conventional approach for the PC extraction involves the computation of the input data covariance matrix and then the application of a diagonalization procedure to extract the eigenvalues and the corresponding eigenvectors.  
For each plot, the PCA was applied on each one of the four SAR temporal series presented in Section 3.2 (\(\sigma^{0}_{\text{P,VV}}\), \(\sigma^{0}_{\text{P,VH}}\), \(\Delta\sigma^{0}_{\text{PC,VV}}\), \(\Delta\sigma^{0}_{\text{PC,VH}}\)). Since each temporal series consists of 82 different values, we obtain 82 different PC values for each time series. Finally, each plot is defined by 328 different PC values (\(82\times 4\)).  
For optical images, the PCA was also applied to the NDVI temporal series of each plot. Since the temporal series consists of 17 NDVI values, the PCA produces 17 different PC values for each plot. The obtained 17 PC values are later used in a random forest classifier in order to classify irrigated plots.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 6 Simulation Results Discussion
### PAPR Performance  
As shown in Figure 9, traditional OFDM symbols will cause a high PAPR, thereby reducing the system transmission performance. Although the conventional OTFS symbol reduces the PAPR to a certain extent, the PAPR of the transmission system is still very high. The proposed improved selective mapping algorithm can significantly reduce the PAPR of OTFS symbols with lower complexity. It can be seen from the figure that when the Complementary Cumulative Distribution Function (CCDF) is \(10^{-3}\), compared with the traditional OFDM system, our proposed scheme can obtain a 2.5-dB PAPR reduction; compared with the conventional OTFS system, our proposed scheme can obtain a 1.0-dB PAPR reduction.  
Figure 8: Independence test results.  
Figure 9: CCDF of PAPR for different schemes.
## 2 Materials and Methods
### Evaluation Methods
#### 2.5.2.1 Production of Global SIF Composites  
To facilitate the comparison of retrieval results, this study generated two types of SIF products and vegetation indices at two temporal resolutions (annual and monthly) and two spatial resolutions (2\({}^{\circ}\) and 0.1\({}^{\circ}\) grid cells). The 2\({}^{\circ}\) grid cells followed the established strategy for generating GOSAT composite SIF, while the 0.1\({}^{\circ}\) grid cells corresponded to GOSAT's spatial resolution.  
Monthly composite SIF was created by directly averaging daily SIF within each grid cell. For annual composite SIF, normalization of lighting conditions based on solar zenith angle (SZA) was applied to balance significant illumination differences between months for comparison with vegetation indices. Obvious outliers were filtered out using uncertainty values. The formula for calculating the uncertainty \(\sigma\overline{\text{F}}\) of composite SIF is given by  
\[\sigma\overline{\text{F}}=\sqrt{\frac{1}{\sum_{i=1}^{n_{\text{f}}}(1/\sigma_ {\text{e},i}^{2})}} \tag{8}\]where \(\sigma_{\rm e}\) represents the uncertainty of individual retrieval results calculated through Equation (7), and n\({}_{\rm f}\) is the number of retrieval results in the grid cell after quality screening. [PERSON] et al. (2013) removed composite SIF with \(\sigma\overline{\rm F}>0.07\) mW\(\cdot\)m\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)nm\({}^{-1}\) for 2\({}^{\circ}\) grid cells. We scaled this threshold for screening composite SIF at other temporal and spatial resolutions.
# A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network
## 5 Conclusions  
This paper proposed an NLOS error mitigation algorithm for tracking a mobile node based on the TOA measurements in a mixed LOS/NLOS environment. An improved MPDA algorithm with a stronger robustness was proposed in this paper. We applied the improved MPDA filter to the IMM-EKF algorithm framework. In this paper, an extended Kalman filter was used in the LOS model, and an improved MPDA filter was used in parallel with the NLOS model. The second filter discarded the position estimates with large errors through NLOS recognition to mitigate the NLOS errors. After updating the model probability of each model, the state estimation calculated using the two filters was based on the updated model probability weighted combination to obtain the final state estimation. The simulation and experimental results showed that the proposed algorithm outperformed the EKF, IMM-EKF, and MPDA in an NLOS environment. The proposed algorithm had a high positioning accuracy in the case of a small NLOS error probability. In the case of a high probability of NLOS error, the positioning accuracy of the proposed algorithm decreased greatly. In the future, the intended further improvement involved increasing the positioning accuracy of the proposed algorithm in the case of a high probability of NLOS error.  
Methodology, L.C. and M.X.; data curation, M.X. and Z.L.; data curation, M.X. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China under grant no. 61803077, Natural Science Foundation of Hebei Province under grant no. F2016501080 and no. F2015501097, and Fundamental Research Funds for the Central Universities under grant no. N172304024.  
The authors declare no conflict of interest.
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 7 Conclusions  
This paper has presented a method for intrusion detection within the BB84 QKD scheme with a partial intercept-and-resend attack. The proposed approach is based on a theoretical model that considers the \(QBER\) induced via both eavesdropping ([PERSON]'s interception) and inherent quantum system noise. A performance evaluation, conducted using  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(p\) & avg. & min. & max. & \(\sigma\) \\ \hline
0 & \(-0.0026\) & \(-0.0453\) & \(0.0455\) & \(0.0173\) \\
0.2 & 0.1964 & 0.1382 & 0.2699 & \(0.0346\) \\
0.4 & 0.3868 & 0.3098 & 0.5016 & \(0.0458\) \\
0.6 & 0.5988 & 0.4980 & 0.7080 & \(0.0510\) \\
0.8 & 0.7982 & 0.6658 & 0.9057 & \(0.0539\) \\
1 & 0.9953 & 0.8646 & 1.1436 & \(0.0656\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Estimated \(p\), \(n=1536\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(p\) & avg. & min. & max. & \(\sigma\) \\ \hline
0 & \(-0.00059\) & \(-0.0375\) & \(0.0415\) & \(0.0141\) \\
0.2 & 0.1980 & 0.1355 & 0.2704 & \(0.0300\) \\
0.4 & 0.4044 & 0.3076 & 0.4960 & \(0.0387\) \\
0.6 & 0.6011a realistic quantum system simulator with noise, demonstrated the validity of the proposed method in estimating the interception density, which is crucial for detecting [PERSON]'s presence.  
In more detail, the results obtained indicate that the system accuracy is influenced by the actual interception density and the initial key length, \(n\). Specifically, lower interception densities and longer key lengths improve the precision of the detection mechanism.  
Therefore, these findings suggest that the method could be effectively utilized in real-world scenarios to detect eavesdropping activities during the generation of a private shared key, thereby enhancing the security of quantum communication systems. Further work on this topic will include an examination of the viability of the proposed method in actual BB84 applications, such as those described in Section 1.  
Conceptualization, F.F., R.G.G. and M.P.; investigation, F.F.; simulation, A.O. and F.F.; writing--original draft preparation, R.G.G., F.F. and A.O.; writing--review and editing, R.G.G., M.P. and F.F.; supervision, R.G.G. and M.P. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Italian Ministry of University and Research (MUR) in the framework of the FoReLab project (Departments of Excellence) and by the University of Pisa in the framework of the PRA_2022_64 \"hOlstic Sustainable Management of distributed softWARE systems (OSMWARE)\" project.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.