esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion of Explainable AI (XAI) for VGG19 Model
### Comparison of LIME and Grad-CAM Explanations for VGG19
#### 4.1.4 Strengths and Weaknesses  
* Strengths--provides detailed, feature-level explanations. Useful for understanding specific reasons behind individual predictions.
* Weaknesses--can be computationally intensive due to the need for multiple perturbations. May not capture the global context as effectively.  
* Strengths--offers a high-level view of important regions, useful for understanding the model's overall focus. Computationally efficient compared to LIME.
* Weaknesses--less detailed than LIME, may not highlight specific features as precisely.
## 2 Envelope Analysis to Detect Defects in Rotating Machinery  
### Hilbert Transform  
Defects in rotating machinery such as bearings and gearboxes generate periodic impact signals. Envelope analysis of acoustic and vibration signals is used to detect such defective signals. Envelope analysis is first performed by filtering with a frequency band containing the resonant frequency excited by the defects [9; 11; 19; 22]. The envelope is extracted by the Hilbert transform of the filtered signal. Then the envelope spectrum is analyzed to detect defects in rotating machinery.  
For a time signal \(x(t)\), the analytic signal \(z(t)\) can be expressed as shown in Equation (1). When the signal \(x(t)\) is expressed as an analytic signal, it can be expanded to a complex dimension, making it easier to analyze the amplitude and phase. Analytic signals can be used to demodulate amplitude-modulated or frequency-modulated signals.  
\[z(t)=x(t)+j\hat{x}(t) \tag{1}\]  
The Hilbert transform of the time signal is expressed by Equation (2). In the time domain, it can be defined as the convolution of \(1/\pi t\) and \(x(t)\). Equation (3) represents the Hilbert transform in the frequency domain, with the phase of positive frequencies shifting by \(-90\) degrees and those of negative frequencies by \(+90\) degrees [19; 41].  
\[\hat{x}(t)=\frac{1}{\pi t}*x(t)=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{x( \tau)}{t-\tau}d\tau \tag{2}\]  
\[\hat{X}(\omega)=-j\,sgn(\omega)\,\,X(\omega),-j\,sgn(\omega)=\left\{\begin{array} []{cl}j,&\mbox{for }\omega>0\\ 0,&\mbox{for }\omega=0\\ -j,&\mbox{for }\omega<0\end{array}\right. \tag{3}\]
## 3 Results
### Analysis of the Contents and Components of Volatile Compounds  
The contents and components of volatile compounds in the fruits of 24 cultivars of apricot were quantitatively and qualitatively analyzed via HS-SPME-GC-MS. The contents and components of the volatile compounds in the apricots at maturity were compared (Supplementary Table S1). In this study, 429 volatile compounds were detected. The 429 volatile compounds were classified into 16 categories: terpenoids, heterocyclic compounds, esters, hydrocarbons, ketones, aldehydes, alcohols, aromatics, amines, phenols, acids, nitrogen compounds, halogenated hydrocarbons, ethers, sulfur compounds, and other unclassified compounds. Here, terpenids were predominant, accounting for 77 species and 17.95% of all volatile compounds. In addition, 70 esters, 65 heterocyclic compounds, 51 hydrocarbons, 34 ketones, 33 aldehydes, 26 alcohols, and 26 aromatic compounds accounted for 16.32%, 15.15%, 11.89%, 7.93%, 7.69%, 6.06%, and 6.06%, respectively, of all volatile compounds. The above eight types of substances accounted for 89.05% of all the volatile compounds. The remaining volatile compounds included 13 amines, 9 phenols, 8 acids, 5 halogenated hydrocarbons, 4 nitrogen compounds, 4 ethers, 3 sulfur compounds, and 1 other unclassified compound. These compounds accounted for 3.03%, 2.1%, 1.86%, 1.17%, 0.93%, 0.93%, 0.7%, and 0.23% of all volatile compounds, respectively (Figure 1b). Among the 16 types of volatile compounds, terpenids were the main volatile compounds in the 24 cultivars of apricot, followed by esters, heterocyclic compounds, hydrocarbons, ketones, and aldehydes; these compounds accounted for more than 70% of the total volatile compounds. The contents of the ether compounds, sulfur compounds, and other compounds in the fruits of the 24 apricot cultivars were less than 1.00 ug/g (Figure 1c). An analysis revealed a significant difference in the total content of volatile compounds in the fruits of the 24 apricot cultivars. The average total content of the aroma volatiles in these fruits was 179.37 \(\pm\) 49.57 ug/g, and the range of volatile compound contents was 112.76 to 317.36 ug/g. The total content of the aromatic volatiles in the fruit of the 'JNL' cultivar was the highest (317.36 \(\pm\) 26.36 ug/g), followed by 'LPHKD' and 'MTYLK' fruits (242.37 \(\pm\) 29.07 ug/g and 231.63 \(\pm\) 9.68 ug/g, respectively). ZSHYX had the lowest volatile compound content, at 112.76 \(\pm\) 3.99 ug/g (Figure 1d).
## 4 Discussion of Results  
### Land Classification Model Evaluation  
During the model evaluation, emphasis was placed on precision, recall, and the F1-score metrics, prioritising these over accuracy in isolation. As presented in Table 2, the final model demonstrated robust performance across all classes--Figure 5 illustrates the classification results against contemporaneous imagery from Sentinel-1 and Sentinel-2 over three distinct regions. Particular emphasis was placed on the 'bare' class, where both high precision and recall were critical to accurately detect deforested and degraded land.  
The 'Standard Mangroves' and 'Tall Mangroves/Forest' classes exhibited some degree of class ambiguity, as expected, due to the natural overlap and intermixing of mangrove species and forest ecosystems. This is further highlighted in the confusion matrix in Figure 6, where the most significant misclassification rates are between these two classes. A train-test split of (0.7, 0.2, 0.1) was used for training, testing, and validation, respectively. An overall accuracy of 90.56% was achieved with the validation set, underscoring the generalisation of the model given the 90.61% accuracy in the test set.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Bare & 0.97 & 0.93 & 0.95 \\ Standard mangroves & 0.92 & 0.88 & 0.90 \\ Tall mangroves/mixed forest & 0.86 & 0.92 & 0.89 \\ \hline \end{tabular}
\end{table}
Table 2: Classification accuracy assessment results.  
Figure 5: Comparative visual of classification results against co-occurring Sentinel-1 and Sentinel-2 images from December 2023. The first row of data are patches of classification results for three distinct regions. Here red represents the bare land class, green represents standard mangroves, and forest green represents the standard forests. The white areas were identified as masked classes such as urban areas and were not classified. The second row illustrates Sentinel-1 VV backscatter, one band of the model inputs. The third and final rows are Sentinel-2 data are for comparative purposes. Here NDVI is used as a good proxy for vegetation health, where the red areas represent sparse to no vegetation. The final row shows a True Colour Composite of the region.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 6 Conclusions  
In the far-field condition, the key characteristics of massive MIMO channels such as the elevation angle and phase excitation were not captured by conventional massive MIMO in 3-D. In this model, we characterized the performance of the RSC for spaced linear antenna array by considering the scatters which were distributed around the TX and RX. Prior to this, an investigation considering AES was made to show the difference between 3-D beam pattern and 3-D conventional massive  
Figure 8: ECC versus receiving antenna spacing on the beam pattern.  
Figure 7: Correlation versus receiving antenna spacing on the beam pattern.  
MIMO for V, H, and V/H polarizations at the RX. In addition, the result has shown an excellent relation between the derived theoretical and the measured data for received spatial correlation. Furthermore, ergodic correlation capacity was computed as a function of the received spatial correlation between elements. Channel measurement has been studied to examine the potential increase in capacity which can be achieved through a different spatial channel. Our research work in practice provided useful insights about the impact of antenna patterns and channel coefficients on the achievable capacity rate. It also confirmed the potential of elevated 3-D beam pattern in the enhancement of system performance. Furthermore, the channel models could be considered in both theoretical and practical guidance for the establishment of a more purposeful massive MIMO measurement in the future in any kind of antenna pattern.  
This work is supported in part by the Shenzhen-Hong Kong Innovative Technology Cooperation Funding (Grant No. SGLH20131009154139588), and the State Major Science and Technology Special Projects (Grant No. 2015 ZX03001035). [PERSON] is also funded by the Knowledge Foundation (KKS) via the ELECTRA project, the SafeCOP project which is funded from the ECSEL Joint Undertaking under grant agreement \(n^{0}\) 692529, and from National funding.  
[PERSON], [PERSON] and [PERSON] proposed the channel model and contributed in providing experimental evaluations. [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Monitoring of Ground Movement and Groundwater Changes in London Using InSAR and GRACE
## 5 Discussions
### Site-Specific Ground Movement and Its Possible Reasons
Groundwater is abstracted from these boreholes through the chalk aquifer at depths in excess of 70 m. The area marked has an extent of 6.25 km\({}^{2}\) and contains 4319 PS points. The average subsidence for the region is \(-\)1.95 mm/year and, since no other explanation for this subsidence could be identified, it appears groundwater abstraction has caused the area to subside due to compaction of the subsurface aquifer. Our results are close to results obtained by PanGeo Project [39], as the authors obtained subsidence rate of \(-\)1.21 mm/year around the same area, marked as PGGH_London_12. In the Figure 8d, the north-western region exhibits distinct areas of uplift. The area marked has an extent of 82.6 km\({}^{2}\) and 53,503 PS points. The mean uplift for this area is 0.21 mm/year. When we overlap the area on geo-referenced groundwater contour map obtained from EA [11], then it can be observed that the uplift area largely coincides with the area of the rise in groundwater levels. Additionally, the area has many lakes and ponds, like Brent reservoir and Viaduct pond, which helps with groundwater recharge. Thus, it can be concluded that the general uplift trend in the area marked is mainly because of groundwater rebound. However, an uplift of 0.21 mm/year is less and it cannot be said with certainty that for an area of nearly 83 km\({}^{2}\), this value is significant. Additionally, it is not as certain that groundwater rebound is the only reason for uplift, and there may be various factors which determine the land movement, ranging from near-surface to deep-seated mechanisms and from less than a decade to over 100,000 years duration.
## 4 Discussion
### FL Studies for Healthcare Applications
#### 4.6.2 Segmentation Task in FL for Healthcare Applications  
Segmentation tasks with medical images have become an essential clinical task in healthcare applications. The medical image segmentation task is the process of identifying and selecting a region of interest within a medical image. Medical images can be in several forms, such as MRI or CT image scan. There are several published FL studies in medical image segmentation, namely brain tumor disease [38], COVID-19 region [53], and prostate cancer region [50]. The summary of published FL studies on segmentation tasks is listed in Table 5. Specifically, in brain tumor segmentation using brain MRI medical images, [PERSON] et al. [38]applied the FL algorithm with CNN-based architecture for multi-institutional collaboration in brain tumor segmentation tasks while preserving the patient data. Compared to existing collaborative learning approaches, FL achieved the highest dice score of 85% and scaled more effectively as the number of collaborating institutions increases. Using multinational three-dimensional chest CT images from three countries, [PERSON] et al. [53] applied federated semi-supervised learning with 3D u-shape fully connected layer model architecture to segment the COVID-19 disease region. Federated semi-supervised learning can assure good training performance even when some healthcare sites have a limited number of annotated data compared to unannotated data. Additionally, the semi-supervised environment may alleviate some of the strain associated with expert annotation, which is critical given the present pandemic crisis. Similarly, [PERSON] et al. [50] performed prostate segmentation with a 3D anisotropic hybrid network (3D AH-Net) model on MRI with collaboration from industry, public universities, and the federal institution. The proposed FL algorithm experimented with three medical nodes showed a dice score of 88.9%.  
**RQ3**: What are the research gaps and potential future research directions of FL related to medical data?
## 4 Conclusions  
Densified ceramics with a BCZN Perovskite-type structure were prepared by a simple method using the aqueous mixing technique-assisted solid-state method. X-ray diffraction shows that Ba\({}_{5}\)Nb\({}_{4}\)O\({}_{15}\) and/or Ba(Co,Zn)\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\) were found as secondary phases on the surface, depending on the sintering conditions. This study demonstrated that a higher sintering temperature with a shorter holding time followed by annealing steps at lower temperatures with a longer holding time are very important to produce microwave dielectric materials. Excellent microwave dielectric properties were achieved for the coarse-grained microstructures after sintering and annealing steps at 1450 \({}^{\circ}\)C for 2 h and at 1300 \({}^{\circ}\)C for 30 h, respectively. These process conditions represent the optimal sintering and annealing conditions to produced Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) microwave dielectric materials. BCZN ceramics (_S1a_) prepared under these conditions exhibit a high quality factor \(Q_{f}\) of 123,700 GHz measured at 6 GHz, a high dielectric constant \(\varepsilon_{r}\) of 34.5 and a temperature  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Heat Treatment Conditions** & **Density** & \(\varepsilon_{r}\) & \(\tau_{f}\) & \(Q_{f}\) & **Grain** \\
**and Sample Name** & **(g/cm\({}^{3}\))** & \(\varepsilon_{r}\) & **(ppm/\({}^{\circ}\)C)** & **(GHz)** & **Order** & **Size (\(\mu\)m)** \\ \hline Sintering & (_S2_):1350 \({}^{\circ}\)C for 30 h & 6.29 & \(\sim\)34.5 & \(\sim\)0 & 96,132 & **–** & 0.6–2.3 \\ process & (_S1_):1450 \({}^{\circ}\)C for 2 h & 6.44 & \(\sim\)34.5 & \(\sim\)0 & 36,953 & **–** & \(\sim\)14 \\ \hline Annealing & (_S2a_):1300 \({}^{\circ}\)C for 30 h & 6.56 & \(\sim\)34.5 & \(\sim\)0 & 99,700 & **+** & 0.9–4 \\ process & (_S1a_):1300 \({}^{\circ}\)C for 30 h & 6.47 & \(\sim\)34.5 & \(\sim\)0 & 123,700 & **+** & \(\sim\)14 \\ \hline \hline \end{tabular} \(a\) Italic letter corresponds to annealing process of the BCZN ceramics. (**–,+**) Absence, Presence of the characteristic signal of 1:2 cation ordering within the crystal.  
\end{table}
Table 2: Characteristics and dielectric properties (measured at 6 GHz) of BCZN specimens _S1_, _S2_, _S1a_ and _S2a_.  
coefficient of the resonant frequency \(\tau_{f}\) of 0 ppm/\({}^{\circ}\)C. All the characteristics of the prepared Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) suggest its suitability for applications in the microwave range.  
The authors gratefully acknowledge support from the French ANR under Contract No. ANR-08-MAPK-0013 project \"Cheapcomponents\". We wish to thank our industrial partner: TEMEX Ceramics. All the authors have made significant contributions to this study and have approved this submission. [PERSON] and [PERSON] prepared the powders and the ceramics materials. [PERSON], [PERSON], [PERSON] and [PERSON] carried out the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] contributed to analysis and interpretation of data. [PERSON] and [PERSON] prepared the manuscript. The authors declare no conflict of interest.
# Numerical Analysis of the Breakdown Process of CF\({}_{3}\)I at Low Pressure
## 3 Results
### Positive Ion Kinetics
Notably, the densities of I\({}^{+}\) and CF\({}_{3}\)I\({}^{+}\) tend to surpass those of \(CF_{3}^{+}\) and \(CF_{2}\)I\({}^{+}\) during the breakdown stage due to slightly higher ionization reaction cross-sections. The densities of all four positive ions significantly escalate during the breakdown stage, both within the bulk region and the cathode sheath. Notably, the density of positive ions in the bulk region is approximately one order of magnitude higher than that in the cathode sheath. However, a reverse trend is observed for energy, heating rate, and current density. The strong electric field within the cathode sheath accelerates positive ions, resulting in higher energy, heating rate, and current density compared to the bulk region. For instance, for \(I^{+}\), the energy in the bulk region is less than 1 eV, while within the sheath, it elevates to 60 eV. Similarly, for \(CF_{3}^{+}\), \(CF_{3}\)I\({}^{+}\), and CF\({}_{2}\)I\({}^{+}\), the energy in the sheath surpasses that of the bulk region. Ultimately, higher positive ion current densities (\(I^{+}\) is \(-0.2\) Am\({}^{-2}\), \(CF_{3}^{+}\) is \(-0.2\) Am\({}^{-2}\), \(CF_{3}\)I\({}^{+}\) is \(-0.2\) Am\({}^{-2}\), \(CF_{2}\)I\({}^{+}\) is \(-0.1\) Am\({}^{-2}\)) and stronger ion heating rates (\(I^{+}\) is 30 kWm\({}^{-3}\), \(CF_{3}^{+}\) is 35 kWm\({}^{-3}\), \(CF_{3}\)I\({}^{+}\) is 35 kWm\({}^{-3}\), \(CF_{2}\)I\({}^{+}\) is 10 kWm\({}^{-3}\)) manifest in the cathode sheath. Despite the higher density of positive ions in the bulk region, the energy, current density, and heating rate are notably lower compared to those in the cathode sheath due to the electric field shielding effect. In the after-breakdown stage, depicted in Figures 5a-8a, the density of positive ions within the bulk region continues to ascend gradually. The densities of I\({}^{+}\), CF\({}_{3}^{+}\), CF\({}_{3}\)I\({}^{+}\), and CF\({}_{2}\)I\({}^{+}\) within the bulk region all steadily increase to approximately \(10^{16}\) m\({}^{-3}\). In contrast, the density within the cathode sheath remains around \(10^{14}\) m\({}^{-3}\), while near the anode, it diminishes further to approximately \(10^{13}\) m\({}^{-3}\).
# Two-Dimensional Constellation Shaping in Fiber-Optic Communications
## 1 Introduction
Such uniform QAM formats are easy for generation and recovery but suffer a 1.53-dB asymptotic loss towards the Shannon limit. In order to close the gap, constellation shaping was introduced to optical communications. Constellation shaping, including PS [25; 35; 36; 37], geometric shaping (GS) [26; 38; 39; 40; 41], and hybrid probabilistic-geometric shaping (HPGS) [42; 43; 44; 45], is used to mimic a Gaussian distribution with limited constellation points. Although target at approaching Gaussian distribution, PS and GS have completely different generation and detection implementations. GS-QAM is obtained by optimizing some metrics, like mutual information (MI) [40] and generalized MI (GMI) [41], which will relocate the constellation point in geometric space. PS-QAM is applied on a uniform grid, but the constellation points will be transmitted with different probabilities. HPGS can optimize the performance in both geometric space and probabilistic space which should, in principle, provide the optimal performance. In this paper, we review recent advances on 2D constellation shaping in fiber-optic communications. Section 2 gives an overview of PS, GS, and HPGS. Section 3 discusses the performance of PS, GS, and HPGS in Gaussian-noise-limited channels. Finally, the concluding remarks are given in Section 4.
## 3 Results and Discussion
### Parametric Analysis and Descriptive Statistics of the Data
The Endo-nutrient amounts used for the experiment also varied from 0 to 0.4 mL/L, with the mean value calculated as 0.15. A standard deviation of 0.1 was obtained for the datasets. For Na2 HPO4, the amount varied from 0 to 8 g/L with a mean of 3.93 g/L. The standard deviation and variance were estimated at 1.89 g/L and 3.57 g/L, respectively. The biohydrogen production was in the range of 117.46 to 1606.65 g/L, with the mean calculated as 582.98 g/L. The standard deviation and variance were calculated as 493.17 g/L and 243213.28 g/L, respectively.
# Space Habitat Data Centers--For Future Computing
## 6 Feasibility and Performance Evaluation
### Feasibility of Accessing Asteroid Water Resources  
This section presents asteroids whose water can be used for cooling SHDCs. The analysis conducted also examines the feasibility of accessing water from near-Earth-orbiting asteroids. The analysis to be conducted in this case requires accessing data on water bearing asteroids. This investigation is important and should be conducted to identify asteroids that have water which can be accessed and used to realize cooling in SHDCs. The conduct of analysis enabling the identification of these asteroids makes it feasible to achieve the goal of realizing a zero WUE. The data to be used in this regard is that of water bearing asteroid data which is obtained from the Asterank database [49], analysed, and presented in Table 1. Analysis examines the feasibility of mining water from asteroids for a 20-year period as shown in Table 2. The maximum, minimum and mean access intervals are 690 days and 81 days and 319.39 days, respectively. In Table 2, the access interval is in the format \([\mathbf{x};\ \mathbf{y}]\) for dates \([\mathbf{a},\ \mathbf{b},\ \mathbf{c}]\). \(\mathbf{x}\) and \(\mathbf{y}\) are the number of days between dates \(\mathbf{a}\) and \(\mathbf{b}\) and dates \(\mathbf{b}\) and \(\mathbf{c}\), respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**s/n** & **Name** & **Near-Earth Approach Dates** \\ \hline
1 & 1991 DB & 6 March 2027; 29 February 2036; 17 June 2083—three approaches \\ \hline
2 & Seleucus & 24 March 2037; 6 April 2040; 8 May 2069; 27 March 2072–4 Approaches \\ \hline
3 & 1998 KU2 & 15 October 2025; 31 July 2042; 18 September 2069p; 28 June 2086; \\  & 18 October 2096—five approaches \\ \hline
4 & 2001 PD 1 & 3 October 2021; 23 September 2031; 1 September 2041; 1 November 2118—four approaches \\ \hline
5 & 1992 NA & 27 October 2029; 14 August 2055; 25 October 2066; 12 October 2092; \\  & 08 August 2118—five approaches \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of water bearing asteroids and the near-Earth approach dates. Therefore, the data analysis shows that the operation of SHDCs while achieving a zero WUE due to non-reliance on Earth's water resources is achievable and feasible. In addition, asteroid water access enabling the realization of zero WUE SHDC operation can be expected at least 81 days and by at most 690 days after water is accessed from a given asteroid. In a case where multiple asteroids fall within the near-Earth-orbit window, having access to asteroid water after a period of 319.39 days is feasible.