esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 3 Proposed Method
### Denoising Convolutional Autoencoder for Learning Anatomical Structures  
The DAE is one of the dimensionality reduction methods based on deep neural networks, which can be used to extract the anatomical information in the data. In contrast to the conventional autoencoder, the DAE is trained to reconstruct the noise-added inputs to the original input data [28]. Gaussian, masking, and salt-and-pepper noises are commonly used to train the DAE. We considered the use of salt-and-pepper noise since the DAE is trained using binarized segmentation labels in the proposed framework.  
Figure 1 shows the schematic diagram of the adopted DAE. Let \(y\) be a segmentation label (i.e., a ground-truth) used as an input to the DAE. The encoder \(f_{DAE}(\cdot)\) maps the corrupted input \(\hat{y}\) to a lower dimension, and the decoder \(g_{DAE}(\cdot)\) uses these low-dimensional representations to produce the reconstructed input \(\hat{y}\). To generate the corrupted input \(\hat{y}\), randomly generated noise, e.g., salt-and-pepper noise, is added to the clean input \(y\). The loss function \(L_{DAE}\) to train the DAE can be expressed as:  
\[L_{DAE}=L(\hat{y},y) \tag{1}\]  
where the reconstructed input \(\hat{y}\) is computed as \(\hat{y}=g_{DAE}(z;\theta_{g_{DAE}})\) and the compressed representation \(z\) is obtained as \(z=f_{DAE}(\hat{y};\theta_{f_{DAE}})\). Here, \(\theta_{f_{DAE}}\) and \(\theta_{g_{DAE}}\) denote the learnable parameters of the encoder \(f_{DAE}\) and decoder \(g_{DAE}\), respectively. To train the DAE in the proposed framework, we employ the binary cross-entropy as a loss function \(L\). As the training proceeds, the output \(z\) of the encoder contains the abstracted anatomical structure information learned by the data. In other words, the encoder performs an embedding that maps the input space to the anatomical structure space. Therefore, the feature space modeled by the encoder can be used to constrain the embedding space of the segmentation network.
## 3 Results and Discussion
### The ULF Magnetic Variation  
Simultaneously with the study of ENPEMF intensity, continuous monitoring of ULF electromagnetic waves is carried out at the Geophysical Observatory. A highly sensitive inductive magnetic sensor of IMS-008 type is used for the purpose, installed with a north-south orientation, similarly to the ferrite antenna of the geophysical recorder MGR-02-16. At the time of the 4 March 2024 earthquake, the sensor was pointing to the earthquake epicenter.  
Investigation of electromagnetic phenomena in the 0.001-5 Hz ULF band, as a direct signal from the lithosphere, is mostly perspective for revealing short-term earthquake precursors. The main advantage of such a study is that ULF waves may propagate beneath the surface without significant attenuation. Generally, the immediate electromagnetic emission from the lithosphere is concentrated around the frequency \(f=0.01\) Hz [10]. As the authors [10] believe, the achievement of encouraging predictive results is associated with the local ULF monitoring near the epicenter of large earthquakes having a magnitude \(M\) above 6.  
The criteria of objective analysis of seismogenic anomalies in the ULF variation correspond to the conditions of our observation when the magnetic sensor occurred at 12.2 km  
Figure 6: The variation of 250–500 keV gamma rays as measured before the 4 March 2024 \(M6.1\) earthquake. The thin and thick curves represent, correspondingly, the original measurement data and same data smoothed over 10 points. Two straight horizontal lines indicate the value of the mean counting rate (the solid one) and \(-1\sigma\) standard deviation level. The earthquake moment (\(EQ\)) and the dates preceding the earthquake by 9 and 1 day are shown as vertical lines.  
from the epicenter of a \(M6.1\) earthquake, and registration of ULF waves was performed in the 0.0001-20 Hz band. For identification of the effects connected with preparation for the earthquake on 4 March 2024, a dynamic amplitude spectrum of the ULF signal calculated in the range of 0.025-0.066 Hz is shown in Figure 7.  
As seen in Figure 7, on the day of the earthquake, 18.23 min (1094 s) prior to the main shock, a noticeable increment up to more than 2 nT took place in the spectrum amplitude (which is highlighted in the plot with a bold colored line). This value is close to the conclusion of theoretical studies [14; 15], where it was shown that magnetic disturbances of the order of \(1-10\) nT may occur at an epicentral distance of 10-50 km before an \(M\geq 6\) earthquake.  
Further on, at the coming of the seismic wave to the registration point, a sharp outburst occurred in the ULF signal intensity, which resulted from mechanical shaking of the inductive sensor in the magnetic field of the Earth.  
Similar observations of an electromagnetic signal in the band of 0-5 Hz, a few seconds up to several minutes before a seismic event, were considered in [41] on an example of 10 catastrophic earthquakes that happened in Japan, China, Romania, and other seismically active regions.
# Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
## 4 Experiments
### Simulated Experiments
#### 4.1.2 Relation between Layer Distance in CPSFS and Detected Results
(**a**) Side view of reconstructed results. (**b**) Top view of reconstructed results. \(R\) represents results using plenoptic disc radius. \(V\) indicates the results utilizing concept of virtual depth. \(GT\) stands for ground truth. Based on analysis above, we can conclude that it is necessary to keep a close layer distance in CPSFS, in order to ensure the number and accuracy of detected features. Empirically speaking, the \(\Delta R\) can be chosen within \([1,2]\).
## 2 Literature Review
### Construction Supply Chain Configurations: On-Site and Off-Site  
Two distinguishable supply chain configurations can be used to supply the final product to customers: a centralized configuration and a distributed configuration [22]. In a centralized configuration, products are produced at a single location, and the raw material is supplied to that location by the suppliers. The end product is then shipped from that single location to all the customers around the world. In the case of construction, the product (i.e., a building) is constructed from components that are prefabricated by a supplier in a central factory. The supplied prefabricated components are then assembled on the site, and finishing work is performed on them to complete the building.  
The distributed supply chain concept refers to production facilities located near the points of consumption [23]. In this case, multiple factories in different geographical locations produce products near their points of use. In the case of the construction industry, on-site construction represents a decentralized supply chain, with all the components for production delivered to the production site. The construction and assembly then take place at the production site and are completed with finishing tasks. On-site printing is an example of a decentralized supply chain for construction, since the concrete printer is transported to the construction site and moved around the site as required until the construction is complete.  
During the first wave of industrial automation, around the 1980s, the first prefabricated concrete and masonry elements and modules were developed [24]. Prefabrication means assembling building components in a factory or a special facility and then transporting the partially or completely assembled structures to the building location [25]. Prefabrication contributes to standardization and labor reductions [26]. However, it requires extra engineering effort during the design phase and greater initial investment [27]. Moreover, design is limited to combining a set of prefabricated elements.
# Antisense-Mediated Down-Regulation of Factor V-Short Splicing in a Liver Cell Line Model
## 2 Materials and Methods
### Characterisation of the F5 Gene in HepG2 Cells
#### 2.3.3 Multiplex Ligation-Dependent Probe Amplification (MLPA)  
The \(F5\) gene copy number in HepG2 cells was determined by MLPA analysis [34] using the SALSA MLPA Probenix P469 F5 (MRC Holland, Amsterdam, The Netherlands). This kit contains 26 \(F5\)-specific probes covering all \(F5\) exons except exon 2, as well as 9 reference probes recognising other autosomic loci. Genomic DNA isolated from HepG2 cells was standardized to 20 ng/\(\upmu\)L and the MLPA reaction was performed following the manufacturer's protocol. FAM-labelled MLPA amplification products were mixed with GeneScan(tm) 600 LIZ(r) dye Size Standard and Hi-Di(tm) formamide (Life Technologies, Bleiswijk, The Netherlands), denatured and separated by capillary electrophoresis on an ABI 3730 DNA Analyzer (Life Technologies), as previously described [35]. The results were analysed using Coffalyser.Net (MRC Holland).
## 4 Perceived Distance Control in Spatial Audio with Sound-Field-Based Methods
For example, using a practical loudspeaker array for reproduction, [PERSON] and [PERSON] demonstrated that a fourth-order NFC-HOA with appropriate regularization on a near-field source distance coding filter was able to create different distance perceptions in lateral directions to some extent even when the level cue was removed [47]. Because ILD below 3 kHz is a distance perception cue for proximal lateral source, a fourth- or fifth-order Ambisonics is able to provide a distance-dependent ILD cue below 2-3 kHz. Incorporating the level and high-frequency spectral cues, a third- to fifth-order Ambisonics is expected to be able to recreate the major distance perception cues in a free field. An enhanced scheme to improve the accuracy of ILD control in Ambisonics was proposed [48]. In reflective environments, the direct to reverberation energy ratio is a distance perception cue. Some experiments demonstrated that the first-order binaural Ambisonics is sufficient to recreate auditory distance perception in a reflective environment [49]. Based on a similar psychoacoustic principle, in practice, WFS is able to recreate free-field perceived virtual sources at different distances [50], although the experiment, which was based on a dynamic binaural reproduction experiment, indicated that in practice, WFS may cause some artifacts in recreating focused source. As in all cases of loudspeaker-based reproduction, reflections in the listening room may spoil the distance perception information in the sound-field-based methods. However, the field-based methods, including Ambisonics and WFS, are also able to cancel or compensate for listening room reflection [51,52]. The compensation is theoretically valid below the upper frequency limit imposed by Shannon-Nyquist spatial sampling theorem. Incorporation of listening room reflection compensation may improve the accuracy of target distance information reproduction and enhance the perceived distance control in sound-field-based methods. Further investigations and experiments in this area are needed. Overall, practical sound-field-based methods are only able to reconstruct the target sound field up to certain frequency limit and the psychoacoustic principle should be incorporated to recreate different auditory distance perceptions. Because the knowledge of the interaction, cooperation, and redundancy of different distance cues is incomplete, further investigations are needed.
## 3 Results and Discussion
### Structural Characterization
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Deposition Method** & \(\mathbf{D_{eff}^{(11)}}\) **(nm)** & \(\langle\epsilon^{2}\rangle^{1/2}\) & **a (Å)** \\ \hline VTE & & & \\ CdTe/glass & 81 & 1.5\(\times 10^{-3}\) & 6.46 \\ \hline RF-MS & 64 & \(1.4\times 10^{-3}\) & 6.45 \\ CdTe/glass & & & \\ \hline ECD & & & \\ CdTe/Au/Tl/Si & 21 & \(8.3\times 10^{-3}\) & 6.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structural parameters of CdTe thin films deposited by VTE, RF-MS, and ECD, as quantitatively obtained by analytic fitting of the XRD experimental data using the Voigt profile; Deff\({}_{\text{eff}}^{(111)}\) denotes the crystalline coherence length determined from the analysis of (111) crystalline plane, \(\langle\epsilon^{2}\rangle^{1/2}\) is the mean square micro-strain, and a represents the lattice constant. Figure 6: XRD analysis of (111) diffraction peak corresponding to the CdTe thin film deposited by VTE (**a**) and RF-MS (**b**) on a pristine optical glass and by ECD (**c**) on an Si substrate coated by a thin Au film. The experimental data (black dots) were fitted with Voigt profiles (red curves), with the residual of this procedure presented at the bottom of each graph.
# Coupling Efficiency of a Partially Coherent Radially Polarized Vortex Beam into a Single-Mode Fiber
## 2 Coupling Efficiency of Partially Coherent Radially Polarized Vortex Beams
\begin{array}{c}\sum\limits_{s=0}^{\infty}\frac{1}{s!(m-1]+s+1}\left(\frac{1 }{2\omega_{0}^{2}}\right)^{|m-1|+2s}\frac{1}{A_{x}^{|m-1|+2s+3}}\left(\frac{|m- 1|+2s+1}{2}\right)!\left(\frac{|m-1|+2s+1}{2}\right)!\\ +\sum\limits_{s=0}^{\infty}\frac{1}{s!(m+1|+s+1)}\left(\frac{1}{2\omega_{0}^{ 2}}\right)^{|m+1|+2s}\frac{1}{A_{x}^{|m-1|+2s+3}}\left(\frac{|m+1|+2s+1}{2} \right)!\left(\frac{|m+1|+2s+1}{2}\right)!\\ +\frac{\pi}{2\omega_{0}^{2}w_{xA}^{2}}\left[\begin{array}{c}\sum\limits_{s=0 }^{\infty}\frac{1}{s!(m-1|+s+1)}\left(\frac{1}{2\omega_{0}^{2}}\right)^{|m-1| +2s}\frac{1}{A_{y}^{|m-1|+2s+3}}\left(\frac{|m-1|+2s+1}{2}\right)!\left(\frac {|m-1|+2s+1}{2}\right)!\\ +\sum\limits_{s=0}^{\infty}\frac{1}{s!(m+1|+s+1)}\left(\frac{1}{2\omega_{0}^{ 2}}\right)^{|m+1|+2s}\frac{1}{A_{y}^{|m+1|+2s+3}}\left(\frac{|m+1|+2s+1}{2} \right)!\left(\frac{|m+1|+2s+1}{2}\right)!\end{array}\right]\\ P_{c\_odd}&=\frac{\pi^{2}}{2A_{x}w_{x}^{2}w_{xA}^{2}}\left[\begin{array}{c} \sum\limits_{s=0}^{\infty}\frac{1}{s!(m-1|+s+1)}\left(\frac{1}{2\omega_{0}^{ 2}}\right)^{|m-1|+2s}\frac{1}{(2A_{x})^{|m-1|+2s+2}}(|m-1|+2s+1)!!(|m-1|+2s+1)!!\\ +\sum\limits_{s=0}^{\infty}\frac{1}{s!(m+1|+s+1)}\left(\frac{1}{2\omega_{0}^{ 2}}\right)^{|m+1|+2s}\frac{1}{(2A_{x})^{|m+1|+2s+2}}(|m+1|+2s+1)!!(|m+1|+2s+1)!!\\ +\frac{\pi^{2}}{2A_{x}w_{x}^{2}w_{xA}^{2}}\left[\begin{array}{c}\sum\limits_{ s=0}^{\infty}\frac{1}{s!(m-1|+s+1)}\left(\frac{1}{2\omega_{0}^{2}}\right)^{|m-1|+2s} \frac{1}{(2A_{x})^{|m-1|+2s+1}}(|m-1|+2s+1)!!(|m-1|+2s+1)!!\\ +\sum\limits_{s=0}^{\infty}\frac{1}{s!(m+1|+s+1)}\left(\frac{1}{2\omega_{0}^{ 2}}\right)^{|m+1|+2s}\frac{1}{(2A_{x})^{|m+1|+2s+2}}(|m+1|+2s+1)!!(|m+1|+2s+1)!!\end{array}\right]\end{array}\right.\end{array}  \tag{8}\]  
where \(A_{x}=1/\omega_{0}^{2}+1/(2\delta_{0}^{2})+1/w_{xA}^{2}+1/D^{2}\), and \(A_{y}=1/\omega_{0}^{2}+1/(2\delta_{0}^{2})+1/w_{yA}^{2}+1/D^{2}\). Figure 1: Illustrating notation related to the coupling of a beam into an optical fiber. The power of the input PCRP vortex beams at the aperture plane is  
\[P_{a}=\frac{\pi}{4\omega_{0}^{2}}\left(\frac{1}{\omega_{0}^{2}}+\frac{1}{W^{2}} \right)^{-2} \tag{9}\]
## 4 MG Communication Technologies
### Wireless Technologies
#### 4.2.6 Satellite Technology  
Satellite technology is a solution for MG communication in remote places where cellular or other wireless technologies are not accessible. Furthermore, they can be applied as a redundant path for creating backup communication. There are three different satellite technologies, including Low Earth Orbits (LEO), Medium Earth Orbit (MEO), and Geostationary Earth Orbits (GEO). Table 5 shows the characteristics of satellite technologies. The major disadvantages of satellite technologies are high expense and high latency. However, LEO and MEO areas are being developed to improve latency and bandwidth. More service providers represent GEO, including Immarsat, BGAN, Swift, and MPDS while just NEW ICO offers MEO. Iridium and Globalstar are operators of LEO [55; 56].
# An Intelligent Collaborative Charging System for Open-Pit Mines
## 2 Current Development Status of Key Technologies and Equipment in Open-Pit Mining
### System Architecture and Operational Mechanisms of Intelligent Collaborative Charging Methods
#### 3.2.1 Technical Components  
The intelligent cooperative charging method features an independent technical system, with its core components encompassing the complete \"perception decision execution\" chain.  
Sensor-based technical components. Serving as the \"nerve endings\" of the intelligent cooperative charging method, sensor components perform the core functions of environmental perception and status monitoring. This approach employs a multimodal sensor network to achieve comprehensive data collection and transmission throughout the entire  
Figure 3: Structural schematic diagram of explosive charging robot.  
process. Common technical components include pressure sensors, fiber-optic deformation sensors, tension sensors, flow meters, and IMUs (Inertial Measurement Units).  
Positioning and navigation technical components. Precise positioning forms the foundation for autonomous operations in intelligent charging systems. Modern open-pit mines typically employ an \"air-ground\" collaborative stereo positioning architecture, with technological evolution demonstrating a clear progression from standalone GPS to multi-source fusion solutions. Common positioning and navigation components include RTK-GNSS systems, LiDAR-based SLAM technology, and visual QR code calibration systems.  
Technical components of intelligent algorithms. The intelligent algorithms for smart collaborative charging methods have evolved from traditional PID control to current multi-agent collaborative optimization, with their algorithmic architecture undergoing a paradigm shift from centralized to distributed frameworks. This progression encompasses PID pressure control algorithms, inverse kinematics algorithms, path planning algorithms, and more.  
Technical components of collaborative actuation systems. The mechanical actuators of intelligent collaborative charging methods serve as physical carriers for \"human-machine-environment\" interactions, with their technological evolution characterized by modularity, flexibility, and high reliability. The technical components of collaborative actuation systems include hydraulic locking control, positive-pressure airflow regulation, dynamic pipeline compensation, and emergency braking systems.  
In summary, the technical components of intelligent collaborative charging methods span interdisciplinary domains including mining engineering, mechanical engineering, control science, and information technology. Through the synergy of high-precision sensor networks, multimodal positioning technologies, and intelligent algorithms, they enable fully automated and zero-human-intervention intelligent collaborative charging operations in open-pit mines.
## 7 Simulation Results
### Clustering Results  
CH number in the original protocol were compared with the modified approaches, as shown in Figure 10. The new approach will divide the network into compact clusters, and most of the CHs will be near the base station, reducing the network's energy consumption.  
In addition, the new approach will not contain dead CHs; on the other hand, Figure 11 shows the number of dead cluster heads in the original protocols. Therefore, the new approach will make the clustering more efficient as no cluster heads will die.  
The distance summation of CHs to the base station in the original and modified protocols is shown in Figure 12. The cluster heads will remain in the same place, but the number of joined clusters will be changed depending on the position of the cluster head and energy consumption.  
Figure 9: Results of implementing the proposed approaches: LEACH-KNN, SEP-KNN, TEEN-KNN and DEC-KNN. Cluster heads are marked as bold black plus signs, base stations as green x’s, and other nodes as red points.  
The results in Table 2 show the composition of the longest distance between CHs and the base station in the original and modified protocols. After KNN clustering, nodes will choose CHs nearer to the base station. The proposed method has fewer CHs compared to the original protocol. Therefore, the base station of the network will lose information because of dead cluster heads; the proposed approach focused on all nodes joining the cluster heads with the best location and a high energy level.  
Figure 11: Total dead cluster heads in the original protocols.  
Figure 12: Cluster head distance summation to the base station.  
Figure 10: Comparison of total CHs between the original protocol and the modified protocols.
## 4 Results and Discussion
### Defect Characterization Using Air-Coupled Ultrasonic Wave  
Based on the acoustic mode in honeycomb sandwich composite, acoustic response of debonding defect of honeycomb sandwich composite is studied, and the air-coupled ultrasonic characterization method of internal debonding defect of honeycomb sandwich composite is established. In order to accurately analyze response of the debonding defect of honeycomb sandwich composite to ultrasonic signal, a model with pre-embedded debonding defect in epoxy resin is established, as shown in Figure 4.  
Figure 8 shows the sound field distribution of a intact honeycomb sandwich composite model and a honeycomb sandwich composite model with embedded debonding defect at 20.01 \(\upmu\)s. Figure 8a shows the sound field distribution of the intact model at 20.01 \(\upmu\)s, and Figure 8b shows the sound field distribution of pre-embedded debonding defect model at 20.01 \(\upmu\)s. Comparing with Figure 8a and 8b, it is shown that the lamb wave in aluminum honeycomb core wall in direct contact with the debonding defect is seriously attenuated, and the corresponding leakage lamb wave is hardly visible; the transmitted longitudinal wave T1 is significantly attenuated, and the waveform is not visible, as shown in Figure 8b. Considering that the air-coupled ultrasonic vertical transmission method is used in the actual testing, the debonding defect of honeycomb sandwich composite can be effectively characterized by the transmission longitudinal wave T1.  
As can be seen from comparison between Figure 9a,b, the transmitted longitudinal wave T1 can be effectively recognized, and the amplitude of the transmitted longitudinal wave T1 is significantly attenuated in the defect area and the non-defect area. This indicates that the transmission longitudinal wave T1 has a good response to the debonding defect, that is to say, it can effectively characterize the debonding defect based on the transmission longitudinal wave T1. Meanwhile, as can be seen, the A-scan also includes a plurality of multiple model signals, and it is difficult to identify them. However, it can be seen from simulation calculation results of Figures 8 and 9 that the transmission longitudinal wave T1 (transmission initial wave) can effectively characterize the debonding defect of honeycomb sandwich composite material when an air-coupled transmission method is used.  
Figure 7: The simulation result of sound field distribution with 19.32 \(\upmu\)s in honeycomb sandwich composite.