esa-sceva/satcom-chunk-collection huggingface dataset:
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 4 Design of PCEB-MS Protocol
### UCEBA Algorithm
#### 4.2.3 Selection of Final Cluster Head and Cluster Formation
The ordinary sensor nodes select the cluster head that has the minimum communication cost from many neighboring cluster heads, namely, the cluster head with the strongest signal-receiving intensity, to send the message JOIN_CLUSTER_MSG, and to note the cluster head. This cluster head finally constructs the TDMA dispatch using the organization method, which is similar to the LEACH protocol, and transmits data by clustering members to the cluster head. Algorithm 1 shows the process of a sensor node, \(V_{i}\), participating in the cluster head competition and finally forming a cluster. ```
Input: initial energy of the \(E_{0}\) node; residual energy of \(i\) of the \(E_{cut(0)}\) node; distance \(d_{\text{acSK}}\) between the candidate cluster head and the sink; the maximum distance \(R_{\text{max}}\) between the candidate cluster head and the sink; duration of the competition of the candidate cluster head agreed in advance \(t_{0}\) Output: cluster head, unequal radius \(R_{n}\)
```  
**Algorithm 1**UCEBA Algorithm: unequal cluster algorithm
## 3. Results and Discussion
### Experimental Results
#### 3.1.5 Stress-Strain  
Figure 5 shows the stress-strain relations for each analyzed cornea, calculated from the average radius of each one. The stress-strain curves obtained for an increasing pressure are depicted by the solid colored lines, and those corresponding to a decreasing pressure are represented by dashed lines. For Cornea 3, only the increase is shown, as the drop in pressure could not be carried out. In all cases, greater deformation was observed with increasing stress, but it lowered when the internal pressure decreased.  
The area enclosed between both curves is related to the energy lost during the experimental process, which confirmed the corneal viscoelastic character and provided us with a tissue hysteresis measure.  
Figure 6 depicts the stress-strain curves obtained from the average curvature radii when the applied pressure was increased. These curves show a linear behavior between approximately 6 and 15 kPa (this range corresponds to pressures between approximately 1.60 and 3.33 kPa). The stiffness increased up to 50 kPa, and the anterior corneal surface was stiffer than the posterior one. Different authors have reported similar behavior [20; 21; 22; 23; 24].
## 3 Results and Discussion
### Diafiltration  
The removal of salts used in the purification processes is essential for maintaining the enzyme catalytic power, since it is known that large salt concentrations in certain cases can affect and even denature the target enzyme. This evidence reveals the importance of this process [63; 64].  
Thus, the ultrafiltration process was applied in diafiltration mode as an alternative to remove salts and other remaining contaminants at the end of the previously established purification procedures (aqueous two-phase system, precipitation, and ion-exchange chromatography) [1]. The fractions containing the purified enzyme derived from various purification procedures were diafiltered by adding a solute-free solvent (potassium phosphate buffer 0.05 M) and the filtration was stopped when the target salts were not detected in the enzyme extract. The number of cycles (6 cycles), as previously established for ammonium sulfate, was considered equivalent for other salts [65]. Moreover, our research group previously defined that at least five cycles were required for the total removal of the remaining PEG from the aqueous two-phase system in purification steps [21].  
The combination of ultrafiltration and diafiltration processes was effective, particularly for concentrating and desalting the enzyme extract; furthermore, the total recovery of the sample was possible. The \(\beta\)-galactosidase recovery rate via diafiltration, regardless of the salt used, was always greater than 90% (Table 3), thereby indicating that this process is feasible for removing the salts present in the enzyme extract without significantly affecting the enzyme recovery.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Salt** & **Concentration (mol.L)** & **\%R** & **FC(A)** & **PF** \\ \hline Control & - & 108.9 \(\pm\) 4.2 a & 7.15 \(\pm\) 0.4 a & 1.22 \(\pm\) 0.06 a \\ \hline \multirow{3}{*}{ KCl} & 0.01 & 92.4 \(\pm\) 4.1 b & 4.6 \(\pm\) 0.7 b & 0.9 \(\pm\) 0.05 b \\  & 0.05 & 88.3 \(\pm\) 7.3 bc & 5.9 \(\pm\) 0.1 abc & 0.8 \(\pm\) 0.1 b \\  & 0.1 & 73.9 \(\pm\) 4.7 cd & 3.2 \(\pm\) 0.6 c & 0.7 \(\pm\) 0.09 b \\ \hline \multirow{3}{*}{ NaCl} & 0.01 & 85.9 \(\pm\) 3.9 bc & 4.3 \(\pm\) 0.4 c & 0.8 \(\pm\) 0.08 b \\  & 0.05 & 63.7 \(\pm\) 6.1 d & 3.1 \(\pm\) 0.7 c & 0.7 \(\pm\) 0.1 b \\  & 0.1 & 60.2 \(\pm\) 5.7 d & 3.4 \(\pm\) 0.3 c & 0.7 \(\pm\) 0.06 b \\ \hline \end{tabular} Data followed by the same lowercase letters in one individual column are not significantly different (\(p>0.05\)) to the same parameter.  
\end{table}
Table 2: Effects of ionic strength on the enzyme concentration and purification using a 50 kDa membrane at pH 7.5 and 1.5 kgf/cm\({}^{2}\) of operating pressure.  
Another advantage of the diafiltration mode is associated with the possibility of reducing the concentration polarization and membrane fouling effects, in certain cases [66]. Thus, the choice of operating parameters is crucial in achieving good recovery of the enzyme. The diafiltration mode did not provide high purification factors; however, the process was effective for the complete removal of the salts present in the extract.
## 7 Experiments and Results
### Experiment 3: Linear Blending with the RORPO Filter
#### 7.3.2 Comparison of Different Loss Functions  
We have evaluated different types of loss functions and their effects on the segmentation quality. The evaluation was performed on the best-performing setup with the 3D V-Net as a segmenter. The results for all metrics are shown in Table 6.  
The best-performing loss function in our model is the Weighted Dice Loss. It operates with the parameter \(\beta_{l}\) (see Table 2) to tackle the problem of class imbalance. The higher the value of \(\beta_{l}\), the greater the penalty for misclassified voxels. Unfortunately, this also slows down the gradient flow and increases the risk of sticking in the local minima. When \(\beta_{l}\) is in the range of 5-7, optimal outcomes are obtained. We have found \(\beta_{l}=6\) to be the best in our study.
## 2 Principle of the Conveyance System  
### Principle of the Elementary Digital Actuator (EDA)  
The basic component of the micro conveyor is the Elemental Digital Actuator (EDA). The EDA is a silicon structure with five NdFeB Permanent Magnets (PMs) as shown in Figure 1. The mobile part of the actuator is the central PM, called Mobile Permanent Magnet (MPM), with dimensions \(2\,\mathrm{mm}\times 2\,\mathrm{mm}\times 1.2\,\mathrm{mm}\). The MPM lies in a slightly larger square cavity on the silicon (dimensions \(2.2\,\mathrm{mm}\times 2.2\,\mathrm{mm}\times 1\,\mathrm{mm}\)). The stroke of the EDA in both \(x\) and \(y\) directions is defined by this gap between the MPM and the cavity (0.2 mm). The other four surrounding PMs are fixed in the silicon structure, thus named Fixed Permanent Magnets (FPMs). Each FPM is located at 2.6 mm from the MPM side (d\({}_{1}\) in Figure 1). The magnetization of the MPM and FPMs are directed along the \(z\) axis in opposite directions to obtain an attracting magnetic force between them. This configuration creates four equilibrium positions for the MPM at each corner of the cavity, giving a digital actuation nature to the EDA.  
Two pairs of wires under the cavity are used to actuate the MPM along the \(x\) and \(y\) axes. The principle of actuation of the EDA is the Lorentz's force generated by the current carried by these wires and the magnetic field of the MPM. The current used to displace the MPM is called \"driving current\" (I\({}_{\text{d}}\)). We called the wires by its actuating axis, i.e., the \(x\) wires are the ones actuating the MPM in the \(x\)-axis as shown Figure 1. Each pair of wires is printed in different planes of a multi-layer PCB circuit, in order to avoid electrical contact between them. Both actuating wires can operate simultaneously. If both pairs of wires are activated, the MPM will move in a diagonal direction, and both currents are considered as driving currents. If one current is used to displace the MPM and the other generates a force towards the cavity wall, this last current is called \"holding current\". The holding current could be used to reduce the straightness error of the MPM's displacement along the moving axis, as it forces the MPM to stay in contact with the straight silicon wall. Finally, a thin glass layer is placed between the MPM and the PCB to avoid their direct contact and give the MPM a plane surface to slide between the discrete positions.
## 2 Materials and Methods
### Satellite Imagery Selection and Processing
#### 2.5.4 Extraction of the Intertidal Area Water and Land Mask  
We followed an established method, relying on a Sentinel-2 time series to identify and select the intertidal area to be classified [69; 71; 72]. We calculated the normalized difference water index (NDWI; [73]) for each image to enhance the contrast between the water and land pixels (NDWI is obtained from the green (Band 3) and the near-infrared (Band 8) bands). The concept behind this method is that permanent water bodies and land masses present relatively stable conditions (and show NDWI values with low temporal variance), whereas intertidal areas will have a higher variability in the NDWI values across the tidal time series of images as they are subject to cyclic inundation periods. A water and a land mask were then derived to be used in the subsequent steps of the methods, using the value of 0.2 as the variance threshold to identify intertidal areas (Figure S2; see [69; 72]) for methodological details).
# Industrial Internet of Things (IIoT): Trends and Technologies
###### Abstract  
The Industrial Internet of Things (IIoT) integrates sensors, machines, and data processing in industrial facilities to enable real-time monitoring, predictive insights, and autonomous control of equipment. Unlike the consumer Internet of Things (IoT), which focuses more on smart homes, wearables, and personal devices, the IIoT is designed to handle large volumes of high-speed data streams from critical equipment, often under harsh environmental conditions and strict safety or regulatory requirements.  
Key technologies of the IIoT include advanced sensing and connectivity, edge computing, 5G and 6G communication, artificial intelligence, machine learning, digital twins, cybersecurity, interoperability, and application schemes considering specific scenarios. Specifically, high-precision sensors, low-power wide-area networks (e.g., LoRaWAN, NB-IoT), and deterministic MAC protocols ensure robust data acquisition and timely delivery. Processing data near its source reduces latency, lowers bandwidth costs, and supports real-time control loops critical for applications such as robotics and autonomous guided vehicles. Deep neural models trained on historical and streaming data drive anomaly detection, remaining-useful-life estimation, and adaptive control are significant to enhance the capabilities of IIoT applications. With growing connectivity in the IIoT, emerging standards (e.g., OPC UA, MQTT with TLS) and blockchain-backed identity management help safeguard IIoT ecosystems and simplify integration across diverse vendors.  
Current trends in IIoT research include, but are not limited to, uncertainty-aware analytics that are used to distinguish true equipment faults from innocuous noise or missing readings, scalable communication strategies from optimized cache policies in CDNs to adaptive polling in LPWANs to handle ever-increasing device counts, smart IIoT frameworks, blending traditional preventive schedules with data-driven prognostics to minimize downtime and costs, and sustainable applications, such as precision agriculture and resource-efficient greenhouses, leveraging modular IIoT platforms to optimize yields while reducing thee environmental impact.
# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 3 Results and Analysis
### Quantitative Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Task** & **loss** & **IoU** & **Dice** & **Accuracy** \\ \hline \multirow{3}{*}{U-Net} & Training & 0.07 & 0.77 & 0.92 & 0.98 \\ \cline{2-5}  & Validating & 0.39 & 0.67 & 0.60 & 0.95 \\ \cline{2-5}  & Testing & 0.41 & 0.67 & 0.58 & 0.95 \\ \hline \multirow{3}{*}{U-NET++} & Training & 0.10 & 0.77 & 0.89 & 0.98 \\ \cline{2-5}  & Validating & 0.50 & 0.62 & 0.49 & 0.95 \\ \cline{2-5}  & Testing & 0.47 & 0.65 & 0.53 & 0.95 \\ \hline \multirow{3}{*}{U-Net 3+} & Training & 0.09 & 0.77 & 0.90 & 0.98 \\ \cline{2-5}  & Validating & 0.48 & 0.62 & 0.51 & 0.95 \\ \cline{1-1} \cline{2-5}  & Testing & 0.44 & 0.67 & 0.60 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance results of U-Net, U-Net++, and U-Net 3+ in the training and validation sets with an 80%/10%/10% split of the dataset for training, validation, and testing, respectively (Train: n = 4296, Val: n = 68, Test: n = 68). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Train** & **Validate** & **Test** & **Total** \\ \hline Original without cropping and augmentation & 459 & - & 51 & 510 \\ \hline Dataset after cropping & 605 & - & 68 & 673 \\ \hline Dataset with 80–20% split and augmentation & 4296 & - & 136 & 4432 \\ \hline Dataset with 90–10% split and augmentation & 4840 & - & 68 & 4908 \\ \hline Dataset with 80–10–10\% split and augmentation & 4296 & 68 & 68 & 4432 \\ \hline Dataset with 60–40% validation size: 0.1 and augmentation & 2887 & 321 & 272 & 3480 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The size of the dataset during the evaluation of the models. Figure 6: Performance analysis of U-Net for the training and validation sets. The dataset was split to 80%/10%/10% for training and validation, respectively: **(a)** Loss function. **(b)** Intersection over the Union. **(c)** Dice coefficient. **(d)** Accuracy. Figure 7: Performance analysis of U-Net++ for the training and validation sets. The dataset was split into 80%/10%/10% for training and validation, respectively: **(a)** Loss function. **(b)** Intersection over the Union. **(c)** Dice coefficient. **(d)** Accuracy. Accordingly, Table 5 presents the best results of cavity detection by using U-Net 3+ in the training and validation sets of the dataset with a 60-40% split and validating sizes of 0.1. Based on the training and validation of the dataset, Figure 9 illustrates the best performance results. These extensive experiments showed that the data-splitting process did not affect or enhance the result of the model performance. In this study, the performance is dependent on the type of dataset and the target lesion for detection.
# Simulation of the Marangoni Effect and Phase Change Using the Particle Finite Element Method
## 2 Governing Equations and Solution Methods
### Traction Term at the Free Surface  
At the free surface, external forces such as the atmospheric pressure and surface tension forces are applied through the surface traction vector \(\overline{t}\), which is be split into its tangential and normal components:  
\[\overline{\mathbf{t}}=t_{n}\mathbf{n}+t_{t}\mathbf{t}, \tag{8}\]  
where in 2D \(t_{n}\) and \(t_{t}\) are the magnitudes of normal and in-plane tangential surface traction and \(\mathbf{n}\) and \(\mathbf{t}\) are the normal and tangent vectors on the boundary \(\Gamma_{t}\). In this work, only test cases with a Eulerian description were replicated for the purpose of validation. Hence, the free surface is not able to deform in the normal direction and we therefore have  
\[t_{n}=0. \tag{9}\]  
The Marangoni effect is a tangential force caused by a gradient in surface tension which itself is caused by a gradient in temperature. In the case of welding or additive manufacturing, the concentrated heat source on the liquid melt pool creates a large gradient of temperature along the surface, leading to the Marangoni effect, which is found to be a main driving force for convective flows in the melt pool [23; 24]. The Marangoni shear stress \(t_{t}\) can be modeled using the equation  
\[t_{t}=\frac{\partial\gamma}{\partial T}\
abla_{t}T=\frac{\partial\gamma}{ \partial T}(\
abla T-(\mathbf{n}\cdot\
abla T)\mathbf{n}), \tag{10}\]  
where \(\
abla_{t}\) is the tangential component of the spatial derivative that can be recovered by subtracting the normal component of the temperature gradient from the temperature gradient, as shown on the rightmost part of Equation (10). \(\partial\gamma/\partial T\) is a material property that is referred to as the Marangoni coefficient, and can be a function of temperature.
## 4 Results and Discussion  
### Stress-strain Curve of Broken Rock Mass in Different Size Grading Conditions  
The stress-strain relationship curve of broken rock mass under lateral compression with different size grading distributions and with or without impact strength corrosion influence is shown in Figure 8.  
As can be seen from Figure 8, the stress-strain curve of broken rock mass under confined compressive stress exhibits exponential growth, consistent with the results of laboratory experiments and empirical formulas [39; 40; 41]. The stress-strain curve of the broken rock mass can be divided into three stages: Stage I: the initial stage; Stage II: the slow growth stage; and Stage III: the rapid growth stage. In the initial stage (Stage I), the rock mass has a relatively large void ratio, allowing for sufficient displacement and rotation of rock blocks. Therefore, the stress increase in the broken rock mass skeleton is slow, and rock blocks can fully displace or rotate. During the slow growth stage (Stage II), the void ratio decreases, leading to an increased interlocking phenomenon between rock blocks and gradually increasing the stress-bearing capacity of the broken rock mass. In the  
Figure 8: The stress–strain curve of broken rock mass under laterally confined compression.  
Figure 7: Loading method of the broken rock numerical simulation.  
rapid growth stage (Stage III), the void ratio continues to decrease while interlocking forces between rock blocks increase, resulting in rapid strain growth in the broken rock mass.  
When the grading size distribution of a broken rock mass is classified as G5, it indicates a higher proportion of larger particles and a loose arrangement of the structure. As a result, greater stress loading is required to achieve the same strain. This in turn leads to the fastest growth rate in the stress-strain curve for the G5 grading size distribution. Conversely, when the grading size distribution is classified as G4, it signifies a higher proportion of smaller particles and a relatively larger void ratio after compression. This allows for greater displacement space for smaller rock blocks and makes it easier for the broken rock mass to undergo skeleton redistribution. Therefore, the stress-strain curve exhibits the slowest growth rate for the G4 grading size distribution. For G3 grading size distribution, there exists a significant difference in particle sizes where larger rock blocks support the framework while smaller particles tend to fill in the gaps, resulting in a more stable skeleton structure and thus a slower growth rate in the stress-strain curve. In the case of G1 or G2 grading size distributions, both large and small particles are distributed relatively evenly. Consequently, their stress-strain curves exhibit similar characteristics.
## 5 Analysis and Discussion
### Material Removal Mechanism  
Figure 12 shows the partial enlarged SEM images of the scratch. Plastic deformation during the scratch test of SiC single crystals has been consistently described by many researchers when the scratching depth (or normal force effected on the indenter) is small enough [27]. As shown in Figure 12a, large ductile chips were observed on the edge of the scratch, with no cracks within the groove when the lateral displacement ranged from 128 to 134 um (corresponding normal force ranging from 64 to 77 mN). The main deformation behavior was considered to be plastic in this stage.  
As shown in Figure 12b, when the normal force increased to approximately 87 mN, a radial crack extended to the surface of the sample for the first time. Meanwhile, numerous microcracks perpendicular to the scratching direction were found within the groove, because the residual stress where the indenter impacted on the material subsurface caused median crack extension to the groove surface, thus forming the microcrack. As shown in Figure 12c, with the further increase in normal force, the radial cracks propagated steadily, and the distance between the two adjacent radial cracks decreased.  
After that, as shown in Figure 12d, with the continuous increase in normal force, a number of microcracks propagated to the edge of groove and intersected with the radial cracks, possibly resulting in material removal in the form of peeling chips, thereby leading to the mode of removal changing from ductile to brittle. As shown in Figure 12e-f, with a sustained increase in normal force, the brittle fracture began to dominate the material removal, and a large amount of variably sized debris appeared because the crack system was no longer able to stably extend; instead, this was replaced by a dynamic fracture when the applied load exceeded the critical load [35]. The peeling chips and debris obviously contributed to the improvement of the material removal rate, but caused deterioration of the machining surface.  
Figure 11: Linear fitting curve of \(a\) and \(c\).  
The edge damage sizes of five characteristic positions generated relatively large peeling of the scratched groove as measured by SEM and in Figure 13. Figure 14 shows the comparison of the predicted curve obtained from Equation (4) and the experimental data. The results indicated that the experimental data were consistent with the predicted curve.  
Figure 12: Surface morphologies of cracks and damages with different lateral displacements: (**a**) lateral displacement ranging from 128 to 134 \(\upmu\)m, (**b**) lateral displacement ranging from 171 to 177 \(\upmu\)m, (**c**) lateral displacement ranging from 220 to 238 \(\upmu\)m, (**d**) lateral displacement ranging from 290 to 294 \(\upmu\)m, (**e**) lateral displacement ranging from 430 to 444 \(\upmu\)m, and (**f**) lateral displacement ranging from 556 to 570 \(\upmu\)m.
## 3 Results and Discussion
### Duration of Filtration Cycles
Many of the literature reports emphasize the relationship between the abovementioned parameters and the removal efficiency [16; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 56; 57; 58; 59; 60; 61]. The proposed adsorbent can be a subject of further application studies. However, the presented studies indicate its high efficiency in phosphorus removal from highly contaminated. According to results from this study, increased phosphorus concentration in water resulted in better removal, and the best removal was achieved at the pH 6-7 (on average 96.65%).