esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PhenoCam as a Promising Technology for Long-Term and Continuous Phenological Monitoring
GCC and RCC could better demarcate leaf phenology, including SOS/EOS stages, while NDVI and other indices represent more plant growth and vigor with a better correlation of photosynthetic variables such as FAPAR/PAR [47; 48]. This justifies the use of PhenoCam images for (leaf) phenological studies. The comparison of ground-based observations with PhenoCam data consistently demonstrates a strong correlation (90%) across various ecosystems. The versatility of PhenoCam technology allows for the focused monitoring of specific species or multiple species, enabling the visual identification of various stages of plant life cycles, such as leafing (leaf buds, mature leaves, abscission of leaves), flowering (flower buds, anthesis of flowers, abscission of flowers), and fruiting (fruit buds, maturation of fruits, abscission of fruits). This review highlights the potential of PhenoCam as a promising alternative to labor-intensive ground observations. By utilizing PhenoCam cameras, researchers and environmentalists can streamline their data collection processes and contribute to a more comprehensive understanding of ecosystem dynamics. PhenoCam cameras, by replacing labor-intensive ground observation methods, represent a valuable tool for advancing ecological research and conservation efforts in poorer countries in the tropics.
## 3 Results
### Fat Tissue
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & \multicolumn{4}{c}{**SPA**} & \multicolumn{4}{c}{**EPA**} \\ \cline{2-7}  & **Initial** & **Final** & \(p\) & **Initial** & **Final** & \(p\)** \\ \hline FM (kg) & 15.02 & 20.32 & 0.001 & 15.65 & 18.87 & 0.001 \\ FM (\%) & 31.62 & 32.65 & 0.025 & 32.02 & 30.02 & 0.000 \\ FM (percentile, c) & 91 c & 95 c & 0.024 & 91 c & 85 c & 0.000 \\ FFM (kg) & 32.50 & 41.17 & 0.001 & 32.07 & 43.01 & 0.001 \\ FFM (\%) & 68.38 & 67.35 & 0.051 & 67.98 & 69.98 & 0.025 \\ TBW (kg) & 23.76 & 30.21 & 0.001 & 24.30 & 31.51 & 0.014 \\ TBW (\%) & 50.15 & 49.62 & 0.003 & 51.95 & 52.20 & 0.000 \\ SMM (kg) & 18.47 & 23.35 & 0.001 & 18.75 & 24.12 & 0.001 \\ SMM (\%) & 38.66 & 38.10 & 0.514 & 38.54 & 40.12 & 0.000 \\ \hline \hline \end{tabular}  
* EPA—elevated PA group; SPA—standard PA group; FM—fat mass (in kg); c—percentile corresponding to fat mass percentile charts; FFM—fat-free mass; TBW—total body water; SMM—skeletal muscle mass; \(p\)—ANOVA; \(p\leq 0.05\).  
\end{table} 
Table 3: Comparison of the body composition of overweight girls between the initial and final measurements. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Far Mass, \% deg**} & \multicolumn{1}{c}{**Fara Mass, \% deg**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} & \multicolumn{1}{c}{**Fara**} \\ \hline
**S****S****S****S** & **SFA  
Table 6 compares children's body composition with excess body fat (FM \(\geq\) 85 percentile) between their initial and final measurements. Significant changes are visible between SPA and EPA children. In girls, SPA FM increased by 5.4 kg; in EPA, this was by 4 kg (\(p\) = 0.001). In SPA girls, the mean FM increased from 31.95% to 33.05% (\(p\) = 0.025), and in EPA girls, this decreased from 32.23% to 31.20% (\(p\) = 0.02000). In EPA boys, the mean FM decreased from 27.22% to 25.13%, and in SPA boys, it increased from 30.61% to 32.25%. On average, girls exhibited a higher FM increase compared to boys (32.10% vs. 28.80%; \(p\) = 0.010). The greatest mean increase in frequency of FM was observed in SPA boys by almost 2%. The largest mean FM decrease occurred in EPA boys by over 2%.
# An Investigation on the Synthesis of Alkali Activated Materials from Thermally Modified Clays
## 3 Results and Discussion
### Precursor Characterization
#### 3.2.4 Influence of NaOH Molarity  
Figure 6 presents the CS and AD of the MK-binders activated with the Na\({}_{2}\)SiO\({}_{3}\) and NaOH solutions, in which the molarity of the latter was 8 M, 10 M, or 12 M (Table 1). The influence of NaOH molarity on CS development is notorious. Yearly age CS ranged between 2.7 \(\pm\) 0.7 MPa (M8) and 12.2 \(\pm\) 1.5 MPa (M12), being greatly proportional to the NaOH molarity. At 28 days, NaOH 8 M solutions delivering binders with CS did not  
Figure 4: The apparent density and compressive strength of the alkali activated binders at 7 and 28 days of curing as a function of the calcined clay-to-silica fume mass ratio (CC/SF ratio).  
exceed 3.4 \(\pm\) 0.7 MPa, whereas the M10 and M12 binders displayed CS values around 12.5 MPa. The results suggest that in these systems, the optimal alkali environment for MK activation requires NaOH solutions with a minimum molarity of 10 M, if CS values higher than 10 MPa are to be attained. If sufficient alkalinity is not provided, MK dissolution may be partially impaired and the degree of polymerization of the final products may be weakened. Conversely to what was previously suggested [60; 61; 62], no inflection point was observed in CS when increasing the Na+ availability. An excess of hydroxide ions and the deleterious formation of Na\({}_{2}\)CO\({}_{3}\) did not seem to exist to a large extent as no efflorescence was visible on the surfaces of the M10 and M12 samples. AD was reduced in all binders with curing, but after 28 days, the M12 binders were slightly denser. It is also interesting to note that the WA and OP values increased as the NaOH molarity decreased, even at similar AD values (Table 5). This may suggest modifications in the pore connectivity and tortuosity favored by higher Na+ availability. For better understanding of the morphology, distribution, and connectivity of pores, microstructural analysis and BET analysis can be suggested and will pursued in future works. Reducing the NaOH molarity, and thus the (Na\({}_{2}\)O + K\({}_{2}\)O)/SiO\({}_{2}\) molar ratio, also slightly enhanced the binders' viscosity and workability, but 12 M solutions were selected for further experimentation to maximize the yearly CS in the CRC binders.  
Figure 5: The apparent density and compressive strength of the alkali activated binders at 7 and 28 days of curing as a function of the sodium hydroxide-to-sodium silicate mass ratio.  
Figure 6: The apparent density and compressive strength of the alkali activated binders at 7 and 28 days of curing as a function of sodium hydroxide molarity.
# Dermoscopy Images Enhancement via Multi-Scale Morphological Operations
## 3 Proposed Algorithm  
The proposed algorithm, called _Multi-scale Geodesic Reconstruction based Top-Hat transform_ (MGRTH), is presented in this section. Additionally, all operations are presented in detail, step by step.  
Let \(I\) be the image, \(H\) be the structuring element and \(n\) be the number of iterations. The proposed algorithm is divided into five stages.  
**First stage:**: the bright structures at level \(i\) are extracted by \(RWTH_{m}\) as follows:  
\[RWTH_{m}=\rho_{(WTH(I,mH))}(RWTH^{(m)}(I)), \tag{8}\]where \(WTH(ImH)\) is the mask obtained by Equation (3), \(RWTH^{(m)}(I)\) is the marker obtained by Equation (7). Each \(RWTH_{m}\) represents the \(m\)-level of bright structures of the original image \(I\) controlled by \(m=\{1,2,3,\ldots,n\}\) which is the size of the structuring element, and the dark structures at level \(m\) are extracted by \(RBTH_{m}\) as follows:  
\[RBTH_{m}=\rho_{(BTH(I,mH))}(RBTH^{(m)}(I)), \tag{9}\]  
where \(BTH(I,mH)\) is the mask obtained by Equation (3), \(RBTH^{(m)}(I)\) is the marker obtained by Equation (7). Each \(RBTH_{m}\) represents the \(m\)-level of dark structures of the original \(I\) controlled by \(m=\{1,2,3,\ldots,n\}\) the size of the structuring element.
**Second stage:**: the light residues \(SW_{m}\) are extracted from the dark structures at levels \(m\) and \(m-1\) and the dark residues \(SB_{m}\) are extracted from the light structures at levels \(m\) and \(m-1\) as follows:  
\[SW_{m-1}=\left\{\begin{array}{ll}RWTH_{m}-RWTH_{m-1}&\text{case }m=2\\ RWTH_{m}-SW_{m-2}&\text{for }m>2\end{array},\right. \tag{10}\]  
\[SB_{m-1}=\left\{\begin{array}{ll}RBTH_{m}-RBTH_{m-1}&\text{case }m=2\\ RBTH_{m}-SB_{m-2}&\text{for }m>2\end{array}.\right. \tag{11}\]
**Third stage:**: The maximum bright scaled details are computed from the bright structures extracted at the first stage by \(RWTH_{m}\), and the maximum dark scaled details are computed from the dark structures extracted at the first stage by \(RBTH_{m}\) as follows:  
\[SRPTH=\sum_{1\leq m\leq n}\{RWTH_{m}\}, \tag{12}\]  
\[SRPTH=\sum_{1\leq m\leq n}\{RBTH_{m}\}. \tag{13}\]
**Fourth stage:**: The maximum light residues are computed from the light residues extracted at the second stage by \(SW_{m}\), and the maximum dark residues are computed from the dark residues extracted at the second stage by \(SB_{m}\) as follows [13]:  
\[SSW=\sum_{2\leq m\leq n}\{SW_{m-1}\}, \tag{14}\]  
\[SSB=\sum_{2\leq m\leq n}\{SB_{m-1}\}. \tag{15}\]
**Final stage:**: the enhanced image \(I_{E}\) is performed per pixel as follows:  
\[I_{E}=I+\omega\times\max(SRWTH,SSW)-\omega\times\max(SRTH,SSB), \tag{16}\]  
where \(\omega\) is a factor used to adjust the level of brightness or darkness added to the image.  
Figure 1 shows the original melanoma images on the left (a,c) and the MGRTH-enhanced images on the right (b,d).
## 1 Introduction
If we group callbacks that have identical periods, the search space can be reduced to \(\Pi_{i=0}^{m}(B_{n_{i}})\), where \(B_{n_{i}}\) is the Bell number of the set i of n callbacks with equal periods and m is the number of sets. However, this number remains exponential. Given the complexity of the problem, we need to use heuristics to reduce the search space. A simple heuristic is to regroup callbacks with the same period (or integer multiples) to be assigned to one executor. Several studies have considered the software clustering problem in the context of real-time systems. Common optimization goals are stack memory usage [11; 12] using genetic algorithms and scheduling algorithms, core load balancing [11], inter-communication overhead [13; 14] using integer linear programming (ILP), data consistency using ILP and heuristic methods [15], and deadline violation [16; 17] using genetic algorithms and the greedy heuristic approach for system resource clustering. Other work proposes regrouping runnables with arbitrary periods on the same task using a greedy heuristic approach combined with linear cost function and schedulability test [18; 19]. This approach may lead to a minimum number of executors needed to schedule overloaded systems while minimizing the stack memory usage and context switch overhead. This paper adopts this approach and adapts it to ROS-2 applications. Contribution: This work adopts a greedy heuristic approach to quickly construct a correct solution to the mapping and resource configuration problem of the ROS embedded system. The heuristic itself is not the central work of this paper. The main contribution is the design of the cost function and the associated schedulability test, which efficiently reduce the clustering complexity. The proposed algorithm assigns a set of callbacks to the executors while preserving the schedulability of the real-time system and simultaneously determines the number of executors required to schedule a set of callbacks, the priority assignment of the executor, and the sequence of callbacks within an executor (Figure 1). First, the two-stage approach clusters callbacks using an exact schedulability test before building an executor with priority assignment. The results of this work are as follows:  
* A formal model to analyze the system. * A low-complexity heuristic algorithm to build executors from a set of callbacks. * A method to assign priority to executors and to define the order of execution of callbacks inside an executor. Organization: This article is organized as follows: Section 2 reviews the relevant related works. Section 3 introduces the ROS-2 architecture, its main concepts, and the scheduling mechanisms. The formulation of the system model is provided in Section 4. Section 5 presents the new algorithm for real-time scheduling and resource mapping for ROS-2. Section 6 discusses the results of the experiments obtained. Section 7 concludes the work and identifies potential perspectives.
# G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud
## 4 Experiments Settings and Results
### Main Results
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Target Model** & **Attack** & **ASR** & **CD** & **HD** & **L2** & **AT** \\ \hline \multirow{8}{*}{**PointNet++**} & PGD & 42.34\% & 0.00512 & 0.02053 & 2.56925 & 1.32729 \\  & I-FGSM & 9.64\% & 0.00009 & 0.00836 & 0.20468 & 1.81929 \\  & SIADV & 52.07\% & 0.00180 & 0.06930 & 1.85480 & 1.81142 \\  & Saliency Map & 13.29\% & 0.00493 & 0.11499 & None & 3.43626 \\  & Add & 8.63\% & 0.00012 & 0.00457 & 0.25674 & 4.56375 \\  & AOF & 73.74\% & 0.00708 & 0.02498 & 3.13824 & 39.04768 \\  & L3A-attack & 30.06\% & 0.00076 & 0.01834 & 0.70428 & 1.24706 \\  & **G&G Attack** & **78.971\%** & 0.00127 & 0.00942 & 0.88586 & 11.17533 \\ \hline \multirow{8}{*}{**DGCNN**} & PGD & 61.95\% & 0.00512 & 0.02053 & 2.56925 & 0.76333 \\  & I-FGSM & 13.37\% & 0.00009 & 0.00836 & 0.20467 & 0.67050 \\  & SIADV & 41.41\% & 0.00180 & 0.06930 & 1.85480 & 1.85480 \\  & Saliency Map & 24.47\% & 0.00495 & 0.11492 & None & 4.04345 \\  & Add & 11.02\% & 0.00012 & 0.00456 & 2.56694 & 4.69601 \\  & AOF & 83.55\% & 0.00702 & 0.02507 & 3.12184 & 1.075699 \\  & L3A-attack & 36.30\% & 0.00076 & 0.01834 & 0.70428 & 0.54858 \\  & **G&G Attack** & **97.20\%** & 0.00136 & 0.0423 & 1.44489 & 10.04447 \\ \hline \multirow{8}{*}{**CurveNet**} & PGD & 40.44\% & 0.00512 & 0.02053 & 2.56925 & 1.12563 \\  & I-FGSM & 12.97\% & 0.00009 & 0.00836 & 0.20466 & 1.14258 \\  & SIADV & 48.58\% & 0.00180 & 0.06930 & 1.85480 & 1.53974 \\  & Saliency Map & 19.21\% & 0.00494 & 0.11536 & None & 2.66501 \\  & Add & 9.97\% & 0.00012 & 0.00459 & 0.25670 & 3.27948 \\  & AOF & 75.12\% & 0.00708 & 0.02513 & 3.13730 & 22.04874 \\  & L3A-attack & 30.11\% & 0.00076 & 0.01834 & 0.70428 & 1.06008 \\  & **G&G Attack** & **95.30\%** & 0.00141 & 0.00410 & 1.47170 & 9.67680 \\ \hline \multirow{8}{*}{**PointCNN**} & PGD & 31.60\% & 0.00512 & 0.02054 & 2.56925 & 1.12270 \\  & I-FGSM & 23.99\% & 0.00009 & 0.00835 & 0.20464 & 1.22103 \\  & SIADV & 21.47\% & 0.00180 & 0.06930 & 1.85480 & 1.31106 \\ \cline{1-1}  & Saliency Map & 36.55\% & 0.00492 & 0.11484 & None & 2.08533 \\ \cline{1-1}  & Add & 23.30\% & 0.00012 & 0.00456 & 0.25681 & 3.46817 \\ \cline{1-1}  & AOF & 49.76\% & 0.00706 & 0.02498 & 3.13396 & 2.89447 \\ \cline{1-1}  & I3A-attack & 23.91\% & 0.00076 & 0.01834 & 0.70428 & 1.48106 \\ \cline{1-1}  & **G&G Attack** & **81.36\%** & 0.00149 & 0.00409 & 1.46869 & 13.32512 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attack success rate (%) of black-box transfer attacks for each method with no defence.
## 2 Materials and Methods  
### Principle of Convergence-Confinement Method  
The convergence-confinement method is a theoretical method developed with the maturity of the new Austrian tunneling method. It can explain the interaction process of surrounding rock and support during tunnel excavation and determine the appropriate time for the support installation. Its theoretical basis includes a Ground Response Curve (GRC), Support Reaction Curve (SRC), and Longitudinal Deformation Profile (LDP), as shown in Figure 1. In Figure 1, \(P_{1}\) is the supporting force of the tunnel wall, \(P_{0}\) is the force generated by the tunnel wall at the moment of tunnel excavation, \(u_{\rm r}\) is the displacement generated at the tunnel wall, and \(u_{\rm max}\) is the maximum displacement generated at the tunnel wall. The Ground Response Curve (GRC) describes the relationship between the displacement of the tunnel wall and the supporting force after excavation. With the continuous increase of the displacement of the tunnel wall, the surrounding rock stress releases accordingly. When the tunnel wall reaches the maximum displacement, the surrounding rock stress wholly releases in theory, and the supporting force of the tunnel wall is 0. The Support Reaction Curve (SRC) reflects that the support stress increases with the displacement of the tunnel wall after the construction of the support structure. When the SRC intersects with the GRC, it indicates that the surrounding rock and the support have reached the force equilibrium state.  
The Longitudinal Deformation Profile (LDP) represents the radial displacement along the tunnel's axis for sections located ahead of and behind the face. It reflects the spatial effect of tunnel excavating. Figure 1 indicates that the surrounding rock has already undergone deformation some distance ahead of the advancing face. When the face moves well away from the monitoring face, the radial displacement reaches the maximum, which means the spatial effect has disappeared. Based on this, a large number of studies have been conducted to investigate the support parameter design [24; 25; 26; 27; 28].  
Based on the current research results, the GRC needs to be obtained by numerical simulation with the stress release method. According to reference [29], [PERSON] and [PERSON] studied the typical supporting structures such as arch, shotcrete, and radial bolt and obtained the specific SRC formula. The LDP can be obtained by a three-dimensional excavation simulation. Because the Xigeda stratum is a particular stratum, there are few studies on its LDP, and there is no curve for reference. Therefore, the numerical method was used to study the LDP of the Xigeda stratum in the next section.
# Development, Experimental Assessment, and Application of a Vacuum-Driven Soft Bending Actuator
## 3 Experimental Results and Discussion  
Experimental assessments were conducted at the Hydraulic and Pneumatic Laboratory at the University of Rijeka, Faculty of Engineering, using the following pneumatic equipment: a CECCATO CSM 7.5 compressor with an air preparation unit, a FESTO VPHI-5L-G18-1V1H-V1-S1D proportional pressure control valve, and a FESTO VN-05-H-T2-PQ1-VQ1-RQ vacuum ejector. For data collection and processing, measurement equipment including the National Instruments NI myRIO 1900 device, Zemic H3G-C3-50 kg-6B load cell, and Intel RealSense Depth Camera D455 were used. The laboratory pneumatic setup used a vacuum ejector to generate vacuum pressure, and for the specified FESTO vacuum ejector, the maximum vacuum pressure was \(-0.8\) bar. Furthermore, the proportional pressure valve was used for precise control of the vacuum pressure in the range of 0 to \(-0.8\) bar for the VSBA. A Virtual Instrument (VI) for controlling the used proportional pressure regulator and collecting measurement data was developed in the LabVIEW programming environment. This pneumatic setup presents a system with direct vacuum control, which is described in detail in our previous study [35]. Two types of experimental measurements were conducted under varying vacuum pressure values and different loading conditions: the first measured the maximum blocking forces at different bending angles, while the second investigated the angular velocity-bending angle characteristic.
## 4 Experimental Results and Discussion
### Self-Intersection Removal on a Single SMPL Model  
To evaluate the validity of our method, we set the pose parameters \(\mathbf{\theta}\) to generate triangular meshes with self-intersection deliberately. Then we performed gradient descent algorithm to optimize the pose parameters \(\mathbf{\theta}\) to remove the self-intersection, the shape parameters \(\mathbf{\beta}\) were the ground truth values from dataset and were fixed during iterations. The learning rate of gradient descent was set to \(1.0\times 10^{-4}\). The number of detection rays was set to \(512\times 512\).  
The process of iteration is visualized in Figure 6. We can see that the gradient calculated via proposed method works effectively in a gradient descent algorithm to minimize the value of SPT, that is to say, minimize the number of vertices in self-intersection region.  
In order to show the necessity of gradient normalization which is presented in Equation (11), an experiment with same conditions as the experiment described above, but without gradient normalization, was conducted. The visualized result is shown in Figure 7. Since there are great differences between the areas of triangles in the body meshes, gradients of vertices differ greatly in magnitudes. This often leads to unstable iterations and unpredictable results and we demonstrated that this problem can be solved by gradient normalization.  
As can be seen from Figures 6 and 7, gradient normalization improves the stability of optimization. Therefore it can be concluded that gradient normalization is significant for 3D pose estimation since unstable iterations often lead to failure predictions.  
Figure 6: Images rendered from the iterative process. First column: Images rendered from initial meshes with self-intersection. Second through fourth columns: Images rendered from optimized meshes every 10 iterations. The self-intersection penalty term (SPT) values in the bottom of each column denote the average SPT of three SMPL models.