esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Evaluation Metrics  
We have adopted precision (\(P\)), recall (\(R\)), and field-level F1 score (\(F1\)) as our evaluation metrics. Precision refers to the proportion of true positives among all samples predicted as positive by the model, while recall represents the proportion of true positives among all actual positives. High precision indicates that the model makes few errors among the samples predicted as positive, but it may miss some true positives. High recall indicates that the model correctly predicts the majority of true positives but may misclassify some  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Quantity** \\ \hline Name & 479 \\ Phone number & 315 \\ Post code & 454 \\ Province & 454 \\ City & 479 \\ District & 317 \\ Road & 215 \\ Room & 223 \\ Building & 77 \\ Unit & 39 \\ Country & 454 \\ \hline Total & 3506 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Item statistics for various categories.  
Figure 5: Samples from COFIE.  
negatives as positives. The F1 score is the harmonic mean of precision and recall, with a high F1 score indicating a good balance between precision and recall. The formulas involved are as follows.  
Precision (\(P\)):  
\[P=\frac{TP}{TP+FP} \tag{1}\]  
Recall (\(R\)):  
\[R=\frac{TP}{TP+FN} \tag{2}\]  
F1 Score (\(F1\)):  
\[F1=\frac{2\cdot P\cdot R}{P+R} \tag{3}\]  
where \(TP\) is the number of samples correctly predicted as positive by the model, \(FP\) is the number of samples incorrectly predicted as positive by the model, and \(FN\) is the number of samples incorrectly predicted as negative by the model.  
The three metrics \(P\), \(R\) and \(F1\) can effectively assess the accuracy of a model, but they do not account for partial overlap, and furthermore, they cannot measure the predicted structure (such as groups and nested hierarchies). Therefore, in the CORD dataset, which has a complex hierarchical structure, we introduce Accuracy based on Tree Edit Distance (\(TED\)) [10; 36], which is an important indicator for measuring the similarity between two trees. In this article, BTED score (\(BTED\)) [37] is used as the evaluation metric. The computation formula is as follows:  
\[BTED=\max\left(0,1-\frac{\text{TED}(pr,gt)}{\text{TED}(\phi,gt)}\right) \tag{4}\]  
where \(gt\), \(pr\), and \(\phi\) represent the ground truth tree, predicted tree, and empty tree, respectively. \(BTED\) evaluates the performance of the model by predicting the ratio of the edit distance \(TED(pr,gt)\) between the predicted tree and the real tree to the edit distance \(TED(\phi,gt)\) between the empty tree and the real tree. The smaller the ratio, the higher the similarity between the predicted tree and the real tree, and thus the higher the \(BTED\) value, indicating that the model can accurately predict the structure of the real tree. Conversely, the higher the ratio, the poorer the prediction performance. If the model performs poorly, the \(BTED\) values are set to be 0 to avoid negative values.
# Recent Advances in Ultrafast Structural Techniques
## 3 Structural Techniques Involving Ultrashort Electron Pulses
### Bright Compact Direct Current (DC) FED Setups
Blue and red traces correspond to compact 100 kV DC FED setups (with flat electrodes) with \(\mathrm{d_{T}}=2\) cm and constant electrostatic fields of \(\left|\mathrm{E_{g}}\right|=20\) MV m\({}^{-1}\) and 10 MV m\({}^{-1}\), respectively. (**c**) Dependence of electron pulse duration (without space-charge effects) as a function of \(\left|\mathrm{E_{g}}\right|\). Electrons in simulations were generated at the cathode surface considering a temporal (longitudinal) Gaussian profile of 6 fs (rms) [14 fs (fwhm)], an initial energy spread of 0.2 eV, and a lateral (transverse) Gaussian spot size of 50 \(\upmu\)m (rms). The solid trace in panel (**c**) was obtained from a fit implementing Equation (20). Figure adapted from Reference [224]. surface with \(\beta_{\rm m}\)= 100, we obtain \(\left|{\rm E_{g,c}}\right|\ \approx\) 60-100 MV m\({}^{-1}\), and therefore \(\left|{\rm E_{g}}\right|<\)60 MV m\({}^{-1}\) should in principle be achievable. A more detailed analysis of these and additional simulations show that the temporal broadening in the absence of space-charge effects follows, Equation (20)  
\[\sigma_{\rm t,\ \Delta\varepsilon_{0}}=\ \rm k\ \frac{\sqrt{\Delta \varepsilon_{0}}}{\left|{\rm E_{g}}\right|} \tag{20}\]  
where \(\Delta\varepsilon_{0}\) is the initial energy spread and k is a constant that depends on the type of initial electron distribution (k = 991 fs MV m\({}^{-1}\) eV\({}^{-1/2}\) for isotropic).] It should be noted that Equation (20) and Equation (16) are equivalent since \(\Delta\varepsilon_{0}\ \approx\ \varepsilon_{0}\), and therefore \(\Delta\rho_{0}\ \propto\ \sqrt{\Delta\varepsilon_{0}}\). Initial preliminary tests at UeIL indicate, however, that achieving \(\left|{\rm E_{g}}\right|>30\) MV m\({}^{-1}\) seems to require ultrahigh vacuum conditions, which are currently out of reach in our FED vacuum chamber designed to obtain a background pressure of \(10^{-7}\) torr. A next-generation FED setup will be developed at UeIL to attain 30-fs multielectron bursts.
## 1 Introduction
A first attempt at the use of differential evolution (DE) with constructal design (CD) was performed in the work of [PERSON] et al. [18], where four different versions of the DE algorithm for the geometrical evaluation of a double T-shaped cavity were investigated. This study observed that the mutation operator DE/rand/1/bin achieved better results than the operator DE/best/2/bin, and the best parameters for crossover rate and amplification factor were CR = 0.7 and F = 1.5. Recent studies that analyze the parameters of the meta-heuristics associated with the CD method show that thealgorithm parameter variations affect the geometrical evaluations and reproduction of the geometries effect over the thermal performance of the system [6; 16; 17; 18]. As a consequence, the investigation of the parameters of the algorithms used for geometric optimization is extremely important to guarantee the adequate achievement of optimal configurations and, mainly, for the reproduction of the effect of degrees of freedom over the performance indicators of the system. In this work, a rigorous investigation of the parameters of the canonical differential evolution algorithm when it is associated with constructal design for geometrical optimization of a double Y-shaped cavity (not previously studied in the literature) is performed. It investigated the parameters of crossover rate, amplification factor, and four different mutation operators. The combination of the algorithm parameters generated 16 versions of the DE that were analyzed. The DE algorithm versions were executed to obtain the optimization of four degrees of freedom. The results of the experiments were analyzed by non-parametric statistical tests of the Kruskal-Wallis and post hoc test of Dunn. Firstly, the Shapiro-Wilk procedure is applied to verify the population distribution. The statistical tools applied make possible the identification and discernment of the parameters that improve the performance of the DE algorithm. In the recent literature, a non-parametric statistical test was used to validate and compare the algorithm results [21; 22]. The parameters recommended in the preliminary work of [PERSON] et al. [18] were compared with new values of F and CR and new mutation operators. The findings contribute to new recommendations about the DE parameters that enhance the algorithm performance associated with the CD method for geometrical optimization of the cavity problem. The best parameters found in this work will be applied to optimize all seven degrees of freedom of the complex double Y-Shaped cavity in a future study.
# Instantaneous Networking Service Availability for Disaster Recovery
## 7 Discussion
### Extended System  
The detailed analyses of I-NSA have been carried out for the LI-Cloud-based networks. When the disaster areas involve a much higher number of LI-Clouds, the clustering of LI-Clouds is a possible way to extend this work for coordination among many Li-Clouds to provide networking services--that is, two or three LI-Clouds form a cluster when these LI-Clouds enable the local service coverage and have been connected with each other. Multiple (two or three) clusters are able to further construct a larger coverage cluster. In this way, the evaluation model discussed is expected to be applied for a large number of LI-Clouds.
## 3 Materials and Methods
### 3D Ground Penetrating Radar  
The implementation of efficient and portable multi-antenna (or multi-array) GPR instruments, accurate synchronized positioning systems (RTK GPS and topographic total stations) and the demand for 3D subsurface imaging are pushing towards a rapidly growing application of 3D GPR to near-surface studies (see e.g., [31; 32; 33; 34]).  
The 3D GPR dataset were acquired on feature 8 and feature 10 using the MiniMIRA array GPR (Mala Geoscience, Sweden) equipped with 5 transmitting and 4 receiving 400 MHz shielded antennas, allowing the collection of 8 parallel profiles with a constant distance equal to 8 cm, thus forming a swath of 56 cm wide. To optimize the spatial resolution, we set a trace spacing also equal to 8 cm, obtaining a constant in-line and cross-line coverage.  
The system was connected with an electromechanical odometer for triggering and with RTK GPS for absolute positioning with centimetric accuracy. This allowed us to perform the topographic correction to the GPR volumes, and properly reconstruct the real geometry, depth and slope of the buried surface, in particular in the case of feature 8, which exhibited a more complex topography.  
We applied a standard processing sequence (zero drift removal, background removal, bandpass filtering, amplitude recovery function encompassing spherical divergence and other attenuation effects) on each swath, to enhance the Signal to Noise Ratio. GPR datasets were processed using glices software (DECO Geophysical, Moscow, Russia) as well as an in-house module implemented in Matlab. Swaths were then interpolated within rectangular grids which were hand selected by the operator. This process allowed us to apply FK time migration of the dataset by means of the Stolt algorithm [35] after velocity estimation through hyperbola matching.  
Comparison of standard 2D data with the results obtained with this system at controlled test sites showed that complex near-surface conditions can be hardly, if at all, imaged by the 2D techniques due to the large amount of cross-line information (i.e., out-of-plane reflections and diffractions) recorded into the 2D profile, and are impossible to be resolved by the imaging process if only in-line information is available. This was the case of the study areas, which are characterized by shallow chaotic conditions and geometrically irregular 3D targets.
## Materials and Methods
### Derivation of Block Level Yield Maps and Predictions of Average Block Yield  
In order to extrapolate the linear relationships identified between the selected 18 trees and the optimal VI for each block to all trees growing within the respective blocks, all non-canopy related pixel information (i.e., inter-row vegetation, soil, shading etc.) were removed. This was achieved by identifying only those pixels specific to the individual tree canopies from a 2D scatter plot (Red versus NIR1) and creating a'mask' to sub-set the imagery. The linear algorithm developed between the optimal VI and yield parameter for each respective block was then applied to the sub-setted pixels, converting them from reflectance to the units of the respective yield parameter. Corresponding with the development of the classified yield maps, the average yield of each block was also calculated by substituting the average pixel reflectance value for each block into the corresponding block yield algorithm. Using this process (Figure 3), the average and total yield for each of the five blocks was calculated for the 2016 and 2017 growing seasons and compared to the actual harvested yield and the prediction made by the growers 'eye ball' estimate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **NAME** & **FORMULA** & **REFERENCES** \\
**1** & Normalized Difference Rededge/Red (NDVI recededge) & (RE - R)/(RE + R) & [43] \\
**2** & Transformed Chlorophyll Absorption in Reflectance Index (TCAR) & \(3\times((\text{RE}-\text{R})-0.2\times(\text{RE}-\text{G})\times(\text{RE}/ \text{R}))\) & [44] \\
**3** & Structure Insensitive Pigment Index (SIPI) & (NIR1 - B)/(NIR1 + R) & [45] \\
**4** & Coastal Blue Structure Insensitive Pigment Index (CB SIPI) & (NIR1 - CB)/(NIR1 + CB) & [45] \\
**5** & Normalized difference Red/Red-edge index (R/REENDVI) & (NIR1 - R)/(NIR1 + RE) & [46] \\
**6** & Normalized difference Red/NIR2 index (R/N2 NDVI) & (NIR1 - R)/(NIR1 + NR2) & [4] \\
**7** & Green normalized difference vegetation index (GNDVI) & (NIR1 - G)/(NIR1 + G) & [47] \\
**8** & Modified Simple Ratio (MSR) & (NIR1 / R - 1)/(SQRT/(NIR1/R) + 1)) & [48] \\
**9** & Ratio Vegetation Index (RVI) & NIR1/R & [34] \\
**10** & Normalized Difference Vegetation Index (N1 NDVI) & (NIR1 - R)/(NIR1 + R) & [33] \\
**11** & Normalized Difference Vegetation Index (N2 NDVI) & (NIR2 - R)/(NIR2 + R) & [33] \\
**12** & Normalized difference red edge index 1 (RENDVII) & (NIR1 - RE)/(NIR1 + RE) & [49] \\
**13** & Normalized difference red edge index 2 (RENDVII) & (NIR2 - RE) & [50] \\
**14** & RDVI1 & (NIR1 - R)/(SQRT (NIR1 + R)) & [51] \\
**15** & RDVI2 & (NIR2 - R)/(SQRT (NIR2 + R)) & [51] \\
**16** & Transformed difference vegetation index (TDVI) & \(1.5\times((\text{NIR1 - R})/SQRT(NIR1 + R + 0.5))\) & [52] \\
**17** & Transformed difference vegetation index 2 (TDVI2) & \(1.5\times((\text{NIR2 - R})/SQRT(NIR2 + R + 0.5))\) & [52] \\
**18** & Non Linear Index (NLI) & (NIR2 - R)/(NIR2 + R) & [53] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vegetation indices used in this study.
## 3 Results and Discussion
### Characterization of Zr-EPRC
#### 3.2.3 Brunauer-Emmett-Teller (BET) Analysis  
A BET analysis revealed that the specific surface area of Zr-EPRC was 56.01 m\({}^{3}\)/g, with a pore volume of 0.092 cm\({}^{3}\)H/g and an average pore diameter of 7.290 nm. The adsorption-desorption isotherms and pore size distribution (Figures 11 and 12) further  
Figure 10: EDS spectrum analysis graph of Zr-EPRC.  
Figure 9: SEM images of Zr-EPRC. The magnifications from (**a–d**) are 2000, 5000, 10,000, and 25,000.  
confirmed the irregular mesoporous nature of the material, characterized by an H\({}_{3}\)-type hysteresis loop in the adsorption isotherm.
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 4 Results and Discussion
### Influence of Fluid-Injection Scheme
#### 4.4.5 Influence of Variation Frequency  
In this section, the impact of injection frequency on the amplitude of downhole pressure fluctuations will be analyzed. With the injection frequency as the variable, simulations were conducted on the amplitude of downhole pressure fluctuations at depths of 1000 m and 3000 m. The simulation results are shown in Figure 19a,b. From the simulation results, it can be observed that when the well depth is smaller, the amplitude of downhole pressure fluctuations exhibits a fluctuating state, which is particularly evident in Figure 19a. The analysis suggests that the reason for this is the proximity of the injection frequency to the natural frequency of the wellbore. Therefore, it can be concluded that, similar to the average injection flow rate, there is an optimal solution for the injection frequency, and this optimal solution is related to the natural frequency of the wellbore.  
In conclusion, the friction loss between the fracturing fluid and the inner wall of the pipe plays a dominant role in affecting the downhole pressure fluctuations in the variable-flow-rate hydraulic fracturing of horizontal wells. Therefore, exploring methods to reduce the friction loss between the fracturing fluid and the inner wall of the pipe should be one of the important research directions for future variable-flow-rate hydraulic fracturing.  
Figure 17: Influence of the mean flow rate on the amplitude of downhole pressure fluctuations: (**a**) 1000 m deep; (**b**) 3000 m deep.  
Figure 18: Influence of fracturing fluid dynamic viscosity.
## 4 Pathophysiological Mechanisms Linking OSA and Periodantal Disease
### Intermittent Hypoxia  
Disturbed sleep and intermittent hypoxia related with OSA may activate vascular endothelial markers such as E-selectin and s-ICAM-1, impairing immune function and increasing vulnerability to infections, including those caused by periodontal pathogens [25; 53]. However, the precise role of these endothelial and immune system alterations in periodontal health remains largely speculative [15].  
Intermittent hypoxia, a hallmark of OSA, drives the generation of reactive oxygen species (ROS), leading to oxidative stress and activating inflammatory pathways, including the NF-kB signaling cascade [10; 54]. This activation triggers leukocyte recruitment and amplifies the release of pro-inflammatory cytokines, potentially contributing to the destruction of periodontal tissues. Elevated levels of markers such as high-sensitivity C-reactive protein (hs-CRP) and interleukin-1 beta (IL-1\(\beta\)) have been documented in the serum and gingival crevicular fluid of OSA patients; however, direct correlations with periodontal breakdown require further investigation [54; 55]. Intermittent hypoxia could exacerbate oxidative stress and inflammation in periodontal tissues, promoting alveolar bone resorption, periodontal inflammation, and tissue degradation.  
Beyond localized effects, intermittent hypoxia induces systemic changes, including insulin resistance, glucose intolerance, hormonal dysregulation, and sympathetic nervous system overactivation. These systemic alterations may intensify inflammatory responses, adversely impacting both general and periodontal health [15; 56; 57]. Evidence from animal models reinforces this link, as sleep-deprived rats exhibit accelerated gingival inflammation and alveolar bone loss, suggesting that hypoxia exacerbates periodontal tissue deterioration [57; 58].  
Overall, these findings highlight a complex interplay between OSA-induced inflammatory cascades, oxidative stress, and immune system alterations that may facilitate periodontal tissue breakdown. However, the exact causal mechanisms remain poorly understood, necessitating further research to elucidate the pathways through which OSA exacerbates periodontal disease.
## 2 Materials and Methods
### SWE Multiphysics Model
#### 2.2.2 Imaging Sequence  
The imaging sequence simulation in the multiphysics approach is illustrated in step 3 (Figure 2). The basis of the ultrasound simulation is Field II, in which tissue is represented by a collection of random point scatterers reflecting the ultrasonic waves emitted by the modeled probe. For each emitted beam, the scatterer's position is updated based on the CSM extracted displacement fields, utilizing first a temporal interpolation from the CSM timescale to US timescale and subsequently spatial interpolation from CSM mesh grid to US scatterer grid. In order to obtain a proper random distribution of point scatterers within our numerical phantom, we used an algorithm based on the open-source software Visualization ToolKit (VTK) [31]. This algorithm first generates randomly distributed scatterers in a box surrounding the phantom's geometry and then removes the abundant scatterers outside the actual geometry based on geometric criteria of the scatterers relative to the phantom's surface [32]. Approximately 10 scatterers per resolution cell (with its size calculated based on receive F-number, transmit frequency and pulse length) were considered to ensure a Gaussian-distributed RF signal [33].  
To mimic our SWE experiments (see Section 2.1), two ultrafast imaging settings were simulated, one with and one without coherent plane wave compounding, using the same probe parameters as mentioned in Table 1. However, the virtual transducer's size was reduced to 128 piezoelectric elements to decrease computational time. For the same reason, the number of simulated frames was limited to 27 and 9 for the single and compounded Plane Wave Imaging (PWI) acquisition, respectively. For the estimation of the scatterer displacement during these simulations, the displacement information of the same CSM simulation was used since the pushing parameters or location did not change throughout the experiments (see Table 1). In our simulation setup, each transducer element was divided into four rectangular mathematical elements in the elevational direction to ensure a far-field approximation of the spatial impulse response. Channel data were acquired at a fast time sampling rate of 100 MHz, IQ-demodulated to 32 MHz and subsequently delay-and-sum beamformed with parameters mentioned in Table 1 using an in-house developed code from the Norwegian University of Science and Technology (NTNU). The obtained wave propagation from these simulations is termed 'virtual imaged shear wave propagation' throughout this work (see Figure 2).
## 8 Conclusions
The proposed ZTA architecture, while effective in reducing unauthorized access, could still be vulnerable to sophisticated insider threats, as continuous authentication and behavioral analytics have inherent challenges in detecting highly motivated adversaries. Additionally, the energy optimization algorithm, while efficient, may require further refinement to ensure its applicability in diverse deployment scenarios, such as resource-constrained edge environments. Lastly, the privacy mechanism, though adaptive, may need additional tunable parameters to further balance privacy and model accuracy in more dynamic environments.
\begin{table}
\begin{tabular}{c c} \hline \hline
**Flight Plan Type** & **Advantages/Drawbacks** \\ \hline \multirow{4}{*}{**Circular**} & More difficult to realize the trajectory \\ \cline{2-3}  & Trajectory easy to follow with a UAV \\ \cline{2-3}  & More difficult to stitch images together (flight direction vector changes) \\ \cline{2-3}  & Areas with higher probability of detection are searched first \\ \hline \multirow{4}{*}{**Standard**} & Commonly supported by flight planners \\ \cline{2-3}  & Advantageous if part of the terrain is completely inaccessible \\ \cline{2-3}  & Search sequence uncorrelated with area probability of detection \\ \cline{2-3}  & Trajectory hard to follow (for fixed-wing UAV) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Standard vs. circular flight plan. Figure 3: User interface for search and rescue missions after refreshing the available satellite map presentation with new images acquired with the unmanned aerial vehicle (UAV) (highlighted patch with the red border). Thus, in this paper, we propose a system based on a commercial data link for the real-time UAV-GS transfer of high-resolution images in non-urban areas. A set of tests were designed and carried out to confirm the viability of the proposed solution. Our solution provides new data for the GIS platform to visualize and plan a search, even in areas that are not covered with Global System for Mobile Communications (GSM) signals or other means of data transfer. The paper is organized as follows. Section 2 provides a review of the relevant literature and methods used in this type of problem, while Section 3 describes the conducted tests for all used methods. Section 4 contains all the experimental results together with a detailed analysis, and Section 5 concludes the paper and discusses future intentions for research, as well as potential improvements.