esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 2 Study Area, Data, and Methodology
### Data
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Scale/Resolution** & **Source** \\ \hline Elevation & 10 m & [[https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)]([https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)) (accessed on 10 November 2024) \\ Slope & 10 m & [[https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)]([https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)) (accessed on 10 November 2024) \\ Aspect & 10 m & [[https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)]([https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)) (accessed on 10 November 2024) \\ Plan curvature & 10 m & [[https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)]([https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)) (accessed on 10 November 2024) \\ Profile curvature & 10 m & [[https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)]([https://finitaly.pi.ingv.it/](https://finitaly.pi.ingv.it/)) (accessed on 10 November 2024) \\ Lithology & 1:100,000 & [[https://doi.org/10.1594/PANGAEA.935673](https://doi.org/10.1594/PANGAEA.935673)]([https://doi.org/10.1594/PANGAEA.935673](https://doi.org/10.1594/PANGAEA.935673)) (accessed on 26 November 2024) \\ Soil type & 1000 m & [[https://esdac.jrc.ec.europa.eu](https://esdac.jrc.ec.europa.eu)]([https://esdac.jrc.ec.europa.eu](https://esdac.jrc.ec.europa.eu)) (accessed on 26 November 2024) \\ Land use & 30 m & [[https://zenodo.org/records/3986872](https://zenodo.org/records/3986872)]([https://zenodo.org/records/3986872](https://zenodo.org/records/3986872)) (accessed on 26 November 2024) \\ Distance to roads & 100 m & [[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)) (accessed on 29 November 2024) \\ Distance to rivers & 100 m & [[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)) (accessed on 29 November 2024) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data sources and spatial resolutions of static environmental factors used in this study. Figure 1: Geographic and climatic characteristics of Italy: (**a**) topography and landslide distribution; (**b**) mean annual precipitation from 1981 to 2020. To ensure consistency among all datasets, we resampled all spatial layers to a uniform resolution of 1 km using ArcGIS 10.8. Additionally, only landslide events and rainfall records from 2003 to 2020 were retained to match the temporal coverage of the SoMo.ml-EU, soil moisture data, with events outside this period excluded. Figure 2: Spatial distribution of static environmental factors used in this study: (**a**) elevation; (**b**) slope; (**c**) lithology; (**d**) soil type; (**e**) land use; (**f**) aspect; (**g**) plan curvature; (**h**) profile curvature; (**i**) distance to roads; (**j**) distance to rivers.
## 4 Circular Plate Undercoated with Radially FGM
### Numerical Solution  
By employing the values \(R=1\) m, \(R_{1}=0.2R\), \(R_{2}=0.8R\), \(h=0.05\) m, \(z=0.02\) m, \(v=0.3\), \(E_{0}=210\) GPa, \(T_{0}=50\)\({}^{\circ}\)C, \(\alpha=1\times 10^{-5}\)\(/^{\circ}\)C, the analytical results were assessed and validated using numerical solutions obtained through the finite element method. The stress variations of the FGM-undercoated circular plates are presented in Figure 10 for different \(P\) values (\(P=0,\ 1,\ 2\)), in Figure 11 for different \(n\) values (\(n=0,\ 0.5,\ 1,\ 2,\ 3\)), and in Figure 12 for different \(m\) values (\(m=0,\ 1,\ 2\)).  
Figure 10: The distributions of stresses \(\sigma_{r}\) and \(\sigma_{\theta}\) of the radially FGM-undercoated circular plate (\(n=2\)) subjected to radially (\(m=2\)) and different transversely thermal loads (\(P=0,\ 1,\ 2\)).  
Upon comparing Figures 10-12 to Figures 2-4, respectively, it becomes evident that the mechanical behavior of the FGM-coated circular plate differs from that of the FGM-undercoated circular plate, which is formed by simply adding a homogeneous ring to the outer radius of the former. This difference may be attributed to a squeezing effect caused by the added ring, leading to an increase in stress in the FGM-undercoated layer, particularly for larger values of \(n\) and \(m\), as well as smaller values of \(P\). These findings suggest that the addition of a homogeneous layer to an FGM-coated circular plate significantly affects its mechanical response.  
Exploring the influence of the FGM-undercoated layer thickness provides an additional intriguing aspect to consider. Figure 13 exhibits the stress distribution of the FGM-undercoated circular plate for different undercoated thicknesses, represented by \(R_{1}/R=0.1,\ 0.2,\ 0.3,\ 0.5\) and \(R_{2}=0.8R\). Notably, a decrease in \(R_{1}/R\), indicating an increase in FGM-undercoated thickness, corresponds to a decrease in the maximum stress within the FGM-undercoated circular plate. It suggests that thicker FGM-undercoated layers lead to lower maximum stress levels. Furthermore, upon comparing Figures  
is evident that the stress behaviors of both the FGM-coated and FGM-undercoated circular plates exhibit similar patterns.
## 3 Smart and Secure HIE Systems: SSHES
### Blockchain and Integration into HIE Systems
#### 3.3.2 Private Blockchains in HIE Systems  
A private blockchain is a blockchain network limited to a defined set of participants. Unlike public blockchains, which are open and decentralized, private blockchains are permissioned and overseen by a single organization or a consortium of organizations. Access to a private blockchain is restricted, requiring participants to be granted permission to join the network and utilize its features. In the last decade, several private blockchains have been released, e.g., Hyperledger Fabric, R3 Corda, Quorum, Corda Enterprise, Hyperledger Besu, MultiChain, etc. Thus, integrating such a private blockchain into an HIE system can lead to numerous benefits such as enhanced security and interoperability and increased transparency [16; 28; 29]. However, such integration requires careful planning, design, and implementation. By defining clear objectives, designing an appropriate private blockchain network, establishing data standards and protocols, developing smart contracts and applications, ensuring compliance and security, and conducting pilot testing, healthcare organizations can leverage the benefits of blockchain technology to enhance data exchange, interoperability, and patient-centric care.
## 4 Discussion
As shown in Figures 3 and 7, whenever the drone approaches the obstacle and there is a transition from fixed to floating conditions, the last coordinates surveyed before the switch are characterized by comparatively high errors (decimeters). These cases are due to the lack of GNSS signals or poor signal quality due to the multipath and sky obstruction. By looking at the bottom plots in Figures 3 and 7 reporting the VRMS, it can be noticed that in these cases, such parameters evidence the deterioration of the coordinate's quality. Therefore, using a threshold on these parameters would help the navigation system to become aware of the approaching critical conditions, too. In terms of the time to recover the ambiguity-fixed condition after flying far enough from an obstacle, the tests showed that it is usually necessary to wait about 9-13 s. These time spans were calculated with respect to the instant the cart passed the average position where the GNSS fixing was lost approaching the obstacle and considering the drone moving back at a walking speed (about 1-2 m/s). The presence of tall walls (in test-2 and test-3) negatively impacted the time to re-fix the solution. In contrast, in test-1, the boundary conditions in terms of surfaces inducing a multipath caused the occurrence of some significantly longer fixing times. Table 1 summarizes three practical recommendations that drones users should consider when flying in conditions critical to the sky visibility. Finally, it is worth noting that the RTK-positioning technique is a kind a three-dimensional relative positioning of the rover receiver with respect to the master one. Therefore, when the RTK works properly, the drone's positions are coherent between themselves at a level of a few centimeters, but their overall accuracy also depends on the quality of the coordinates assigned to the master station. This means that, in the case of the automatic flying of the drone, following a pre-defined plan around the surveyed object, the coherence of the master's coordinates to those used to survey the object itself is fundamental. This can be achieved by setting the master station over a benchmark surveyed with centimeter-level accuracy, potentially using a geodetic-class GNSS receiver or a whatever topographic technique linking its coordinates to the same reference system used to plan the flight.
An Orthogonal Model to Study the Effect of Electrospraying Parameters on the Morphology of poly (d,l)-lactide-co-glycolide (PLGA) Particles  
[PERSON]  
1 Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou,  
Tao-Yuan 33302, Taiwan; [EMAIL_ADDRESS]  
2 Department of Mechanical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan; [EMAIL_ADDRESS] (C.-H.F.); [EMAIL_ADDRESS] (Y.-W.L.)  
3 Department of Orthopedic Surgery, Chang Gung Memorial Hospital-Linkou, Tao-Yuan 33305, Taiwan 3302,  
with electrospraying, nevertheless, the optimization of the spraying procedure has been based on a time-consuming trial-and-error process.  
In past three decades, PLGA has been one of the most attractive polymeric materials used to manufacture devices for drug delivery and tissue engineering applications [15]. PLGA is biocompatible and biodegradable, possesses a wide range of erosion times, has tunable mechanical strengths, and most importantly, is a FDA approved polymer. Crystalline PGA, when co-polymerized with PLA, reduces the degree of crystallinity of PLGA, and as a result, increases the rate of hydration and hydrolysis. As a rule, a higher content of PGA leads to quicker rates of degradation, with an exception of 50:50 ratio of PLA/PGA, which exhibits the fastest degradation. A ratio of 50:50 PLGA have been extensively studied for the development of devices for controlled delivery of small molecule drugs, proteins and other macromolecules in commercial use and in research [16].  
An empirical study, based on a fractional orthogonal array design, was completed to investigate the effect of different processing factors on the size distribution of electrosprayed particles. 50:50 PLGA materials were selected. Experiments were carried out on an electrospraying setup consisting of a syringe and needle, a ground electrode, and a high voltage supply. After electrospraying, the particle size and morphology were characterized by a scanning electron microscope. A fractional orthogonal was designed based on a design matrix proposed by Dr. [PERSON] [17]. It allows one to examine a selected subset of combinations of multiple factors at multiple levels, but with less experimental test trials for optimization of the electrospraying process.
## 4 Thermographic Techniques for Breast Cancer Detection
### Dynamic Infrared Thermography
#### 4.2.3 Rotational Breast Thermography  
Very often in conventional breast thermography, the patient placed in front of the camera is imaged in three different views, namely contra-lateral, medio-lateral oblique, and axillary. However, in these views, the lower posterior regions of the breast are not imaged completely because of the normal breast sag [20]. Consequently, cancerous lesions in such regions are not detectable. To overcome this limitation, a novel technique, known as rotational breast thermography, was suggested. This technique foresees that the patient is lied in prone position on a special set up known as the Mammary Rotational Infrared Thermographic System (MAMRIT), with one breast freely suspended into a temperature-controlled closed chamber through a small circular aperture. Also inside this chamber is a robotic arm, within which an infrared camera is fixed, that rotates clockwise around the suspended breast so that it can be imaged from many perspectives. In such a way, the whole breast surface can be completely analyzed, ensuring that all abnormalities are detected.  
The potential of rotational thermography for the automatic detection of breast abnormalities involving a cold stimulus was evaluated by [PERSON] et al. [20]. In particular, these authors assessed the accuracy of this technique both before and after the application of a cold stress that was performed by cooling the temperature of the closed chamber by 2-3 \({}^{\circ}\)C. A group of 36 patients (24 healthy and 12 with breast abnormalities) were involved in this study. Also, steady-state thermograms were recorded after a period of rest to stabilize basal metabolic activity, with the air temperature inside the chamber adjusted to the comfort level of the patient and a relative humidity of 60%. In both steady and dynamic conditions, a series of 12 images were acquired for each breast at spatial intervals of 30\({}^{\circ}\). After that, statistical and texture features were extracted from the rotational thermogram series in both conditions. Then, these features were used as inputs within a support vector machine able to automatically classify healthy and malignant breasts. The authors reported better accuracy in steady conditions (about 83%) when compared to that obtained (about 70%) from the thermograms recorded after the cold stimulus. In conclusion, the authors stated that rotational thermography holds great potential as a screening tool for breast cancer detection, especially when it is used as an adjunct tool to ultrasound.
## 3 Fuzzy-Logic Autonomous Navigation System
### Behavior Rule-Evaluation Process  
In the rule-evaluation process, the behavior rules consisted of \"if-then\" statements, such as \"if(l\({}_{d}\) and f\({}_{d}\) and r\({}_{d}\) and \(\theta_{d}\)) then (LVel and RVel).\" LVel and RVel are the output values of each behavior rule and are used to generate the real control signal of the speed of the two wheels. In this study, 81 behavior rules, consisting of navigation skills, movement towards the target, following of obstacle edges, and avoiding of obstacles, were designed for the stable motion control of the robot during navigation. Table 1 lists some of the behavior rules to make the robot move toward the target. Table 2 lists some behavior rules to make robot follow the edge of obstacles. Table 3 lists some behavior rules for the robot to avoid obstacles. In the rule-evaluation process, only rules with all non-zero weights for the input variables, f\({}_{d}\), r\({}_{d}\), l\({}_{d}\), and \(\theta_{d}\) were activated because the output of each behavior rule is the minimum weight value among the input variables.  
Figure 5: Performance of sensor systems of mobile robot: (**a**) description of distance sensors; (**b**) description of positioning system.  
Figure 6: Membership functions for the fuzzification of input data: (**a**) membership function for the fuzzification of distance data; (**b**) membership function for the fuzzification of positioning data.
## 5 Conclusions  
Numerical calculations have been carried out on the combined heat transfer induced by thermal conduction, natural convection and surface radiation in the porous char layer that forms from intumescent coating under fire. The isothermals, streamlines and average Nusselt number are obtained at different parameters as follows: the Rayleigh number (\(Ra\)), conductivity ratio (\(Kr\)), emissivity (\(\varepsilon\)), radiation-conduction number (\(Nr\)), void fraction (\(\phi\)) and heating mode. The following conclusions could be drawn:  
1. The Rayleigh number and the conductivity ratio have a positive effect on natural convection. As \(Ra\) increases, the air flow in the cavity is much stronger and the natural convection heat transfer is strengthened due to the increase in buoyancy force. The increase in the conductivity ratio results in an increase in the walls' temperature difference. Then, natural convection, which is driven by the temperature difference, is strengthened. In addition, the effect of \(Kr\) on the heat transfer always has a limit when it reaches a high level.
2. The emissivity and radiation-conduction number have a significant effect on the total heat transfer when the temperature ratio is a constant. With increasing \(\varepsilon\) and \(Nr\), surface radiation is enhanced. This leads to a decrease in the walls' temperature difference, which will certainly inhabit natural convection in the cavity. In addition, \(Nr\) plays a more important role in a certain range, and the impact on the heat transfer will be limited when \(Nr\) is too low or too high.
3. The void fraction of the structure also affects the total heat transfer. The change in \(\phi\) will cause a change of both the temperature difference and characteristic length. Natural convection and surface radiation are strengthened by the combined effect of temperature difference and characteristic length with the increasing void fraction.
4. The heating mode has a remarkable effect on the total heat transfer. Proper use of the heating mode (which means the proper direction of the heat flow in this study) is able to cause stronger air flows. As a result, natural convection in the cavity will be strengthened but surface radiation among the walls will, as a result, be suppressed.  
Conceptualization, Y.H. and M.L.; methodology, L.Z.; software, L.Z.; validation, Y.H., L.Z., and M.L.; formal analysis, L.Z.; investigation, Y.H.; resources, Y.H.; data curation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z.; visualization, L.Z.; supervision, Y.H.; project administration, M.L.; funding acquisition administration, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Nature Science Foundation of China, Grant No. [51706213].  
Not applicable.  
Figure 15: Streamlines and isothermals obtained when heating the bottom, \(W_{\max}=69.235\).
# Effect of Different Bonding Materials on Flip-Chip LED Filament Properties
## 3 Results and Discussion
### Shearing Force of the Solder Joint  
Figure 5 shows the shearing force for FC-LED filament solder joints obtained with SAC0307 and Sn90 Sb10 solders. The shearing force fir both solder joints was over 200 gf (standard shearing force). Figure 5 shows that the average shearing force for the Sn90 Sb10 solder joint was higher than that for the SAC0307 solder joint. The average shearing force for the SAC0307 filament solder joints was approximately 305.25 gf, and that for the Sn90 Sb10 filament solder joints was approximately 340.35 gf; therefore, that for the Sn90 Sb10 filaments was 11.5% higher than that for the SAC0307 filaments. We also found that the lower quartile of the SAC0307 shearing force was 268 gf, and the upper quartile was 337.5 gf. The lower quartile of the Sn90 Sb10 filament shearing force was 313 gf, and the upper quartile was 380.5 gf, while the median linear shearing forces of SAC0307- and Sn90 Sb10-welded solder joints were 313 gf and 339.5 gf, respectively. The shearing force of a solder joint can be calculated by the formula:  
\[\delta=\sqrt{\frac{\sum\left(X-\mu\right)^{2}}{N}}\]  
where \(\delta\) is the variance, \(X\) is the shearing force of the sample, \(\mu\) is the average shearing force, and \(N\) is the number of solder joints. The shearing force variance value \(\delta\) of the SAC0307 sample was calculated to be 49.99, and that of the Sn90 Sb10 sample was 41.67. These data revealed that the shearing force of the Sn90 Sb10 filament solder joint was more concentrated and superior to that of the SAC0307 filaments.  
Figure 4: Microstructure of the two types of solder joint: (a) SAC0307 and (b) Sn90 Sb10.  
Figure 5: Shearing force of the two types of solder joints.  
Compared with SAC0307, Sn90 Sb10 has higher bonding strength, and the melting point of Sn90 Sb10 is 10 \({}^{\circ}\)C higher than that of SAC0307. During the flip-chip bonding process, the wettability and spread of the SAC0307 solder are worse than those of the Sn90 Sb10 solder [22; 23]. In the eutectic welding process, the thermal fatigue resistance of the SAC0307 solder is poorer than that of the Sn90 Sb10 solder, which adversely affects the strength of the solder joint. In addition, there are more voids in the SAC0307 solder joints, which leads to more cracks. It can be concluded that the Sn90 Sb10 filament solder joint has fewer voids and cracks, which improved the reliability of the Sn90 Sb10 filament solder joints [24; 25; 26].
# Research on Seismic Signal Denoising Model Based on DnCNN Network
###### Abstract  
Addressing the noise in seismic signals, a prevalent challenge within seismic signal processing, has been the focus of extensive research. Conventional algorithms for seismic signal denoising often fall short due to their reliance on manually determined feature functions and threshold parameters. These limitations hinder effective noise removal, resulting in suboptimal signal-to-noise ratios (SNRs) and post-denoising waveform distortion. To address these shortcomings, this study introduces a novel denoising approach leveraging a DnCNN network. The DnCNN framework, which integrates batch normalization with residual learning, is adept at swiftly identifying and eliminating noise from seismic signals through its residual learning capabilities. To assess the efficacy of this DnCNN-based model, it was rigorously tested against a curated test set and benchmarked against other denoising techniques, including wavelet thresholding, empirical mode decomposition, and convolutional auto-encoders. The findings demonstrate that the DnCNN model not only significantly enhances the SNR and correlation coefficient of the processed seismic signals but also achieves superior noise reduction performance.  
CNN (convolutional neural network); residual dense module; signal noise reduction; SNR (signal-to-noise ratio) 2020 ac academic Editor: [PERSON]  
1