esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Experiments, Results and Discussions
### Experiments Settings
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Instance** & **Opt** & **Algorithm** & **Best** & **Worst** & **Average** & **StD** & **N\({}_{1\%}\)** & **Ave.time (s)** \\ \hline \multirow{2}{*}{eil51} & \multirow{2}{*}{426} & **DICA1** & **426** & **432** & **427.25** & **1.3717** & **19** & 15.49 \\  & & DICA2 & 590 & 770 & 700.6 & 43.9167 & 0 & **14.77** \\ \hline \multirow{2}{*}{berlin52} & \multirow{2}{*}{7542} & **DICA1** & **7542** & **7542** & **7542** & **0.00** & **20** & 19.69 \\  & & DICA2 & 11,034 & 14,074 & 12,229.55 & 736.6680 & 0 & **16.87** \\ \hline \multirow{2}{*}{st70} & \multirow{2}{*}{675} & **DICA1** & **675** & **683** & **676.7** & **2.5976** & **19** & 15.05 \\  & & DICA2 & 1300 & 1702 & 1518.2 & 93.2386 & 0 & **14.54** \\ \hline \multirow{2}{*}{eil76} & \multirow{2}{*}{538} & **DICA1** & **538** & **546** & **540.75** & **2.5521** & **18** & 16.65 \\  & & DICA2 & 972 & 1298 & 1180 & 70.0669 & 0 & **15.86** \\ \hline \multirow{2}{*}{pr76} & \multirow{2}{*}{10,8159} & **DICA1** & **108,159** & **109,085** & **108,350.65** & **316.9696** & **20** & 15.36 \\  & & DICA2 & 228,851 & 280,746 & 258,438.5 & 1333.2 & 0 & **15.12** \\ \hline \multirow{2}{*}{kroA100} & \multirow{2}{*}{21,282} & **DICA1** & **21,282** & **21,433** & **21,306.5** & **43.0893** & **20** & 18.31 \\  & & DICA2 & 66,573 & 84,480 & 73,032.3 & 4561.7 & 0 & **18.28** \\ \hline \multirow{2}{*}{kroB100} & \multirow{2}{*}{22,141} & **DICA1** & **22,141** & **22,376** & **22,194.45** & **67.5539** & **19** & 18.16 \\  & & DICA2 & 65,905 & 88,172 & 73,587.9 & 5113.6 & 0 & **17.98** \\ \hline \multirow{2}{*}{eil101} & \multirow{2}{*}{629} & **DICA1** & **629** & **643** & **635.35** & **4.8153** & **10** & 29.46 \\  & & DICA2 & 1426 & 1751 & 1574.3 & 82.1956 & 0 & **25.49** \\ \hline \multirow{2}{*}{kroA150} & \multirow{2}{*}{26,524} & **DICA1** & **26,524** & **26,857** & **26,657.5** & **110.5805** & **8** & 54.28 \\  & & DICA2 & 102,953 & 118,004 & **109,867.7** & 4397.2 & 0 & **44.67** \\ \hline \multirow{2}{*}{kroB150} & \multirow{2}{*}{26,130} & **DICA1** & **26,141** & **26,290** & **26,230.1** & **47.8944** & **10** & 64.20 \\  & & DICA2 & 97,697 & 109,665 & 105,396.8 & 4432.1 & 0 & **53.17** \\ \hline \multirow{2}{*}{rl1323} & \multirow{2}{*}{270,199} & **DICA1** & **272,985** & **283,357** & **277,965.4** & **3895.7** & 0 & 6151.8 \\  & & DICA2 & 7,311,486 & 7,491,391 & 7,398,724.4 & 74,062 & 0 & **179.71** \\ \hline \multirow{2}{*}{fl1400} & \multirow{2}{*}{20,127} & **DICA1** & **20,621** & **20,707** & **20,669.4** & **38.24** & 0 & 5607.6 \\  & & DICA2 & 1,196,412 & 1,226,156 & 1,208,172.6 & 13,065 & 0 & **184.39** \\ \hline \end{tabular}
\end{table} 
Table 2: The results of the experiments. Figure 5: Trend lines of the two algorithms on rl1323. the trend lines of the two algorithms on other instances are not given here. Figures 5 and 6 are utilized as a representative.
# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 1 Introduction
(c) With the development of generative adversarial networks (GAN), it became possible to create arguably pseudo-realistic-looking imagery. [PERSON] et al. investigated the potential for GAN to increase the number of training images based on imbalance and limited labeled data of GPR [32]. While GAN has demonstrated good performance on data augmentation tasks, they are often hard to converge because of mode oscillation or collapse [33]. To improve the quality of generated GPR images and the convergence of the model, [PERSON] et al. proposed a new GAN network by adding convolutional layers to create synthetic images [26]. However, insufficient training datasets can also adversely impact the GAN network used in image generating tasks because training the GAN model requires sufficient data. [PERSON] et al. overcome this problem by using the SinGAN to augment the B-scan images, which can generate new images from a single training image [34]. At present, the traditional augmentation techniques can increase the training data and reduce overfitting when training a deep learning model. It also faces many challenges, such as not being like real GPR images, insufficient variety of images, instability during training, etc. In this paper, we present the data augmentation methods by incorporating domain knowledge. Two typical road subsurface distress (cavity and crack) were collected from the field detection, which was verified by core drilling and industrial endoscope. In addition, the locations of subsurface distress were labeled based on the verified results. Then, the YOLOv7 model was used to study the effectiveness of these data augmentation methods. The proposed data augmentation methods will decrease the values of the loss function and increase the stability of the model.
## 3 Results and Discussion
### BTA Release Studies from Carboxymethyl Cellulose Microspheres
#### 3.5.2 Release Studies
From Table 4, it is possible to verify that values of g(BTA)/g(microparticles) (loading capacity)are considerably lower (approximately six times lower), and therefore, it is most likely that the release of BTA is governed by the swelling of the polymeric matrix. In the present work, we observed a faster release for CMC.BTA particles produced at higher inlet temperatures (180 \({}^{\circ}\)C and 190 \({}^{\circ}\)C). This is most probably associated with a higher evaporation rate of the solvent, due to high inlet temperatures. Solvent evaporation is most likely very fast, hindering the immobilization of BTA within the polymeric matrix remaining mainly at the surface of the microparticles. Furthermore, one can infer that the higher releases observed for higher spray flow rates may also be associated with the concentration of BTA at the droplets surface, since a higher spray flow rate causes higher degree of water evaporation [14; 18]. The proposed microreservoirs are designed to work as an on-demand release device for corrosion inhibitors, to be applied in the development of smart anticorrosive organic coatings. Inhibitor release from the CMC-Na biopolymeric matrix is based on a hydrogel-swelling-driven mechanism triggered by water molecules, which implies that the inhibitor will only be released from the microparticles when in contact with water; otherwise, the inhibitor-containing particles will be stable in the organic-coating formulation. Thus, the developed CMC.BTA microparticles can potentially be applied in protection systems where the presence of water initiates its degradation. For instance, the deterioration of protection systems based on organic coatings starts from the permeation of water through the pores, or micro-cracks, that are formed in the polymeric matrix, which can appear during exposure to the environment. Given the aforementioned findings, it is believed that CMC-Na microparticles with release profiles that show an initial high release of BTA may be adequate for applications that require immediate protection of metal substrates that are known to be highly susceptible to corrosion processes, such as carbon steel. This burst release feature, combined with a gradual release of the remaining inhibitor, which in turn may prolong substrate protection, is achieved with microspheres prepared according to experimental condition 1 (170 \({}^{\circ}\)C and 440 L/h).
# Comprehensive Method to Test the Stability of High Bedding Rock Slope Subjected to Atomized Rain
## 5 Case Study
### Reinforcement Effect of Concrete Plugs  
According to typical curve of displacement surface near the concrete plugs (Figure 14), during the 14 th to 16 th steps, cracks grew down along J\({}_{110}\) and joined with C\({}_{3\text{-}1}\) to form a complete #I-1 slip block; the crack at the front edge rock of LS\({}_{337}\) just opened to #3 concrete plug due to reinforcement effects of the concrete plugs. During the 24 th to 26 th steps, cracks grew downward along f\({}_{101}\) and joined with C\({}_{3\text{-}1}\) to form a complete #I-2 slip block. Meanwhile, cracks grew downward along J\({}_{136}\) and joined with LS\({}_{337}\) to form a complete #II-1 slip block. When the slope exhibited a trend of global instability, all structural planes had been cracked through; the cracks apparently opened and led to the formation of multiple slip blocks.  
The test showed that the concrete plugs could improve the shear resistance of structural planes and inhibit the dislocation between two layers. The displacement magnitude near the concrete plugs was relatively smaller. The slip block reinforced by concrete plugs (such as #12 and #18) would be formed later than without it.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 3 SBAUPT-Based Pedestrian Dead Reckoning (S-PDR)
### Step Length Estimation  
For the step length estimation the model presented in [93] which is based on the Weinberg [81] model was adopted which is shown in Equation (13). A study of different step length estimation models presented in [99] shows that the Weinberg model with personalized set of constants--user and test defined parameters--outperforms other models. The Weinberg model utilizes the maximum and minimum accelerations in the step to estimate the step length based on a conversion unit (\(K\)). The method introduced in [93] utilizes a modified Weinberg model where the conversion unit is adaptive to increase the accuracy of the estimation. The proposed adaptive conversion unit is based on a second-order polynomial of the estimated step velocity.  
\[L_{step}=K_{step}\times\sqrt[4]{A_{max}-A_{min}} \tag{13}\]  
Figure 17: Heading estimation block diagram.  
where, \(L_{step}\) is the step length, \(K_{step}\) is the adaptive conversion unit, \(A_{max}\) is the maximum vertical acceleration in the step, and \(A_{min}\) is the minimum vertical acceleration in the step. The trajectory is then updated using the estimated heading angle and the step length, where the current position is the previous position plus the decomposed step length based on the heading as shown in Equations (14) and (15).  
\[P_{North_{i}}=P_{North_{i}-1}+\cos(\theta_{F})\times L_{step} \tag{14}\]  
\[P_{East_{i}}=P_{East_{i}-1}+\sin(\theta_{F})\times L_{step} \tag{15}\]
## 2 Results and Discussion
### Characterization of Materials
#### 2.1.6 Modes of Interaction Sorbent/Sr(II)  
Crossing the information collected from FTIR and XPS analyses, the speciation of Sr(II) (only free Sr\({}^{2+}\) species under selected experimental conditions), and the titration of the sorbent (pH\({}_{\text{PZC}}\)), it is possible to suggest different modes of interaction of Sr(II) metal ions with AO-APEI (Scheme 1). In acidic conditions, the protonation of amine and carboxylate groups induces electrostatic repulsion and low sorption capacities (competition of protons against Sr\({}^{2+}\) for binding on reactive groups). The increase of the pH (above 3.5) leads to the partial deprotonation of carboxylic acid groups (to form carboxylate anions); the progressive deprotonation of amine groups allows the binding of metal cations. On the other hand, free amine groups are essentially protonated in acidic solutions, and with the increase in pH, the repulsion effect decreases: the binding of Sr(II) may occur on carboxylate groups, hydroxyl groups, free amine groups, and amidoxime sites (including -C=NOH and C-NH\({}_{2}\) groups from the same chain or from vicinal chains). Actually, the co-existence of different reactive groups and their respective influences change with the pH of the solution: protonation/deprotonation, overall charge of the sorbent, and metal speciation. From XPS analysis, Sr(II) may be sorbed by a cation exchange mechanism with Ca(II) bound on carboxylate moieties. Additionally, the high-resolution XPS spectra show that coordinating elements bearing O (hydroxyl groups on saccharide rings, and amidoxime group) as electron-donor groups are also affected by positively-charged Sr(II) binding. Complementary interactions are associated with amine moieties (on amidoxime and on PEI). This diversity of interactions may also explain the relatively high sorption capacities of OA-APEI for Sr(II).
# MCCM: An Approach for Connectivity and Coverage Maximization
## 4 Distributed Maintaining Connectivity and Coverage Maximization (MCCM)
### Neighborhood Discovery Process  
This process builds the neighborhood list for each device \(D_{i}\). Every device will know about the other devices connected directly or indirectly to it. We denote \(\Omega\) the set of n heterogeneous devices in the network:  
\[\Omega=\{D_{i}|i\in[1,n]\}\]  
We denote \(D_{i}\) the \(i-\)th device in the network with center \((x_{i},y_{i})\) and range \(r_{i}\). The distance \(dist\left(D_{i},D_{j}\right)\) between two devices \(D_{i}\) and \(D_{j}\) is the Euclidean distance denoted \(d_{ij}\). We say two devices \(D_{i}\) and \(D_{j}\) are \"neighbors\", if their sensing ranges intersect.  
\(D_{i}\) is directly connected to \(D_{j}\) when \(d_{ij}<r_{i}+r_{j}\) and both devices have minimal overlapping \(\tau_{ij}\) according to [27]. In this case, we denote \(D_{i}\diamond D_{j}\).  
We say \(D_{i}\) is indirectly connected to \(D_{j}\) when there is another device \(D_{f}\), such that \(D_{i}\diamond D_{f}\) and \(D_{j}\) is directly/indirectly connected to \(D_{f}\). We denote this case \(D_{i}\bullet D_{j}\).  
An island (or group) of connected devices to \(D_{i}\) is denoted \(\mathcal{N}_{i}\). It is the set of \(k\) devices that includes \(D_{i}\) and its neighbors as follows:  
\[\mathcal{N}_{i}=\{D_{j}\in\Omega|D_{j}\diamond D_{i}\lor D_{j}\bullet D_{i}\}\]  
where \(|\mathcal{N}_{i}|=k\) and \(k\leq n\).  
Figure 2 shows how the discovery process occurs on a device \(D_{i}\in\mathcal{N}_{i}\).  
Figure 1: \(MCCM\) process steps.  
The main steps are as follows:  
* Any device \(D_{i}\) waits for a certain amount of time \(t\) to emit a discovery signal. It is allowed to make \(c\) trials.
* Upon receiving a signal from a device \(D_{j}\), both devices start the \"Add to neighborhood list\" process, only if they have empty arcs on their circumferences. If so \(D_{j}\) is added to \(\mathcal{N}_{i}\). The devices in \(\mathcal{N}_{i}\) update their list about the newly coming device \(D_{j}\).
* When the emit counter exceeds \(c\) times, the device goes to sleep for a certain period of time \(n\). Then, it tries again until the sleeping count exceeds a value \(s\).
* The discovery process ends when the device does not have any more empty arcs or the sleeping counter reaches \(s\).
## Appendix D Evaluation Resources
### Second Week Evaluation  
1. The following are functions of the Copernicus Emergency Management System (CEMS) EXCEPT: * Disaster evacuation plans
* Generation of rapid maps before, during, and after an emergency
* Forecasting and continuous monitoring of disasters
* Providing information about human settlements and the population
* None of the above
2. Which users from the following list could be authorized to activate the CEMS service? * NGOs
* Public entities
* European Union delegates
* International organizations
* All of the above
3. Classify the following products from the exposure mapping component of CEMS  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**High** & **Moderate** & **Low** & **Challenge** \\ \hline \hline  & & Privacy and ethics due to extensive surveillance use \\ \hline  & & Integration of multisource data \\ \hline  & & Data access while maintaining security and confidentiality \\ \hline  & & Infrastructure maintenance and update \\ \hline \hline
8. & What is the difference between Single Look Complex (SLC) and Ground Range Detected (GRD) images of Sentinel-1? \\
9. & Which of the following portals to access Sentinel data are commercial? \\ \(\bullet\) & CREODIAS \\ \(\bullet\) & Sentinel Hub \\ \(\bullet\) & WEKEO \\ \(\bullet\) & Copernicus Data Space Ecosystem \\ \(\bullet\) & None of the above \\ \hline \hline \end{tabular}
\end{table}
Table 10: Classification of Urbanization and Built Environment Indicators.
# Precision and Reliability of a Dynamometer for Trunk Extension Strength and Steadiness Assessment
## 3 Results  
Figure 4 presents the calibration of the load cell, accompanied by residual analysis, which demonstrated linearity with a coefficient of determination (R\({}^{2}\) = 1), and the applied linear regression yielded a mean square error of 0.55 N and a sum of squared errors of 7.3183 \(\times\) 10\({}^{3}\) N\({}^{2}\). Furthermore, the integration of a High-Resolution Digital Extensometer (HRDE) ensured precise strain measurements, contributing to uniform load distribution and enhancing the overall reliability of the experimental setup. Minimal data variations over time indicated high stability, and the near-zero mean of residuals, alongside a variance of 0.30 N and a standard deviation of 0.55 N, suggests no significant bias in the model.  
Table 1 presents the test-retest reliability results for MVC and SMVC of the trunk extensors.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Test/Retest** & **Day 1** & **Day 2** & \(p\) & **Cohen’s d** & **MDC** & **SEM** & **ICC (95\% CI)** & **R\({}^{2}\)** \\ \hline Maximal voluntary contractions (MVC) & & & & & & & & \\ Peak force & 4.07 & 3.88 & 0.22 & 0.58 & 1.39 & 0.5 & 0.81 & 0.71 \\ (N\(\cdot\)kg\({}^{-1}\)) & (1.18) & (1.23) & 0.22 & 0.58 & 1.39 & 0.5 & (0.62, 0.91) & 0.71 \\ Submaximal voluntary contraction (SMVC) & & & & & & & & \\ Mean Force & 1.88 & 2.07 & & & & & & \\ (N\(\cdot\)kg\({}^{-1}\)) & (0.39) & (0.34) & 0.24 & 0.45 & 1.08 & 0.17 & 0.93 & 0.88 \\ Steadiness & 1.88 & 2.46 & & & & & & & \\ (\%) & (1.22) & (1.37) & 0.83 & 0.44 & 2.36 & 0.85 & 0.87 & 0.40 \\ \hline \end{tabular} Legend: Data are presented as mean (SD), Day 1–Day 2 (test/retest), \(p\)-value, minimal detectable change (MDC), standard error of measurement (SEM), intraclass correlation coefficient (ICC model 3-K) with 95% confidence intervals (CI), and linear regression coefficient of determination (R\({}^{2}\)).  
\end{table}
Table 1: The test–retest reliability results for MVC and SMVC of the trunk extensors during spinal extension.  
Figure 5 presents the Bland-Altman plots for MVC and SMVC, assessing agreement between test and retest conditions within a 95% confidence interval. For peak force, the results were t = 2.7, df = 29, \(p\) = 0.01, with 96.42% of samples within the limits of agreement. For mean force, the results were t = 1.4, df = 29, \(p\) = 0.20, with 96.42% of samples within the limits of agreement. For steadiness, the analysis reported t = \(-\)1.9, df = 29, \(p\) = 0.06, with 92.85% of samples within the limits of agreement.  
Figure 4: Correlation and residuals analysis: (**A**) correlation; (**B**) residuals vs. fitted values; (**C**) heteroscedasticity assessment; (**D**) Q–Q plot of residuals.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 3 Results
### Clinical Metrics  
The Dice score and Hausdorff distance were estimated on both validation and test sets, as reported in Tables 1, 4 and 6. Following the same rule, clinical metrics were computed with respect to the ground truths. The results of the ED and ES volumes and Left Ventricular Ejection Fraction (LVEF) are reported in Table 2. The ED and ES volumes were approximated following the method described in Section 2.3.3. The average results of the validation and test sets are presented in Table 2.  
The computed ED and ES volumes are generally close to the values obtained from the ground truths. However, a small negative bias can be observed both over the ED and ES frames. The volumes from automatic processing are, on average, systematically lower compared to the given ground-truth values. The LVEF percentages show a small overestimation with selected models from data augmentation.  
Lower LVEF differences were obtained over the validation sets, as reported in Table 2. This can be attributed to the higher number of examinations available compared to the test set, resulting in a finer representation of the encountered anatomical variations.
# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
## 4 Discussion
### Accuracy of the Density Prediction Using Void Area Curve
In particular, four textures of soil according to the USDA classification method were used. Therefore, the method has a high level of accuracy similar to that of the existing test method even when considering several water contents and soil textures. As summarized in Table 4, it also showed better accuracy than studies that used deep learning to analyze images. The convolutional neural network had an RMSE of 0.044 to 0.107 t/m\({}^{3}\), while the deep neural network had an RMSE of 0.080 t/m\({}^{3}\). This shows that \(\sigma_{n^{\prime}}\) is a better indicator for predicting \(\rho_{d}\) than the image features used in the deep learning model. Therefore, the \(\rho_{d}\) prediction method using \(\sigma_{n^{\prime}}\) can be widely used as a method for predicting \(\rho_{d}\) because it shows a similar level of accuracy to the existing test method and a higher accuracy than other analysis methods. In this study, only one variable (\(\sigma_{n^{\prime}}\)) was used to predict \(\rho_{d}\). Since various other features can be extracted from soil surface images, it is expected that a multivariate analysis with additional features will improve the accuracy. Comparing the accuracy of the soil density analysis based on vis-NIR spectroscopy [32; 33; 34] with the results of this study, the RMSE is lower and the R\({}^{2}\) is higher, indicating good accuracy. [PERSON] et al. [31] reported that vis-NIR spectroscopy and PLSR successfully predicted various soil properties, but the prediction accuracy was poor for bulk density with a validation R\({}^{2}\) of 0.35. The main advantage of the results of this study compared to the analysis based on vis-NIR spectroscopy is that a high level of density prediction is possible with a common digital camera. Given the widespread availability of digital cameras, this could have very broad applications. However, the digital images used in this study were acquired in a laboratory and their applicability in the field is still unknown. Therefore, further research in the field is needed to further enhance the versatility of this study.