esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In most coal mines in China, the target coabled methane (CBM) and its adjacent CBM generally require joint extraction before the target coabled can be mined. But only the mixed CBM from multi-seams can be measured at the mouth of boreholes, and it is not possible to individually measure the CBM from a target coabled. In view of this issue, the application of isotope geochemistry is proposed for the first time in the industry. Based on the law of mass conservation for the chemical composition of mixed CBM, according to the gas components and isotope difference characteristics of each coabled in multi-seams, a calculation model for the extraction ratio from a target coabled is established. Field experiments were selected in the Xiaotun Mine in Guizhou Province. The extraction ratio of the target coabled 6m determined by isotope geochemistry ranged from 57.81% to 69.58%, and the deviation from the extraction ratio determined by the measured flow method was less than 7%. The residual gas content of the No. 14 mining face was calculated by combining the results of the above two methods, and the deviation from the measured residual gas content was less than 6%, indicating that the results obtained scientifically by isotope geochemistry were reliable. The results of the study can provide a new research tool for the judgment of target CBM extraction standards and, ultimately, ensure the safety of coal mine operation.  
multi-seams; CBM extraction; gas component; carbon isotope; extraction ratio +
Footnote †: journal: Applied sciences  
[PERSON], [PERSON], [PERSON], Q. Application of Isotope Geochemistry in Determining the Extraction Ratio of Target CBM from Multi-Seams.
## 3 Results  
### Patient Experiences  
The demographics of the final sample population were 53% female with a mean age of 51.44 \(\pm\) 20 years (mean age females: 53.31 \(\pm\) 19.9 years; mean age males 48.6 \(\pm\) 20 years). In total, 57.3% of study participants were receiving treatment on more than one tooth.  
There was no significant difference in discomfort by gender (\(p\) = 0.672, F = 0.179). Individuals receiving multiple preparations reported significantly higher discomfort than those receiving a single preparation (\(p\) = 0.0267, F = 5.065). However, overall discomfort scores were low for both groups (mean discomfort less than 2 of 10, see Figure 1).  
Average patient discomfort values were highest for laser treatment without anesthetic (1.6 +/ - 1.8) and a combination of laser treatment and traditional therapy without anesthetic (1.2 +/ - 1.7).  
There was also a low, significant negative correlation between participant age and reported discomfort (\(p\) = 0.0032, R\({}^{2}\) = \(-\)0.3), with older individuals reporting slightly lower discomfort levels (see Figure 2).  
This is corroborated by anesthesia use, where results show that those participants requiring anesthesia during the procedure were significantly younger than those who did not (\(p<0.001\), F = 8.485); however, caution is recommended due to the small sample size for anesthesia recipients (n = 7, see Table 1).  
Figure 1: Discomfort reported for participants receiving one versus multiple preparations. 0 = “no discomfort,” 10 = “extreme discomfort”, * \(p\)-value < 0.05.  
Figure 2: Plot showing patient discomfort regressed on age (R\({}^{2}=-0.3\)).  
For the majority of cases (93%), anesthesia was not requested or required. This was true even when laser use was followed by handpiece use, i.e., nearly half of the anesthesia cases (see Table 1). When asked which treatment option they preferred for future cavity preparations, the majority (98%) would choose the laser over a conventional handpiece treatment: 94% would choose the laser with no anesthetic, and 4% laser with anesthetic. Only 2% of participants would prefer a conventional handpiece treatment with anesthetic for future treatments.
# An Efficient Algorithm for Mapping Deep Learning Applications on the NoC Architecture
## 3 Multilevel Task Mapping for NN Applications
### Level 1 Mapping: Region Mapping  
First of all, we divide the mesh-based NoC into multiple regions, and this will help in identifying the available regions. The regions are selected based on the number of neural network layers to be mapped on the processor architecture. The number of regions are obtained as follows:  
\[n_{r}=\frac{n_{c}}{n_{NN}} \tag{1}\]  
where \(n_{r}\) is the number of regions, \(n_{c}\) is the number of cores, and \(n_{NN}\) is the number of neural network layers.  
We select closely located cores for one region based on the region count obtained by the abovementioned formula. If any or multiple cores are available after this process, then they are merged into a nearby region. In the next step, we identify the regions which are available at the moment by considering a predefined occupancy threshold. This is to select regions that are not occupied at the moment. This makes sure that only those regions should be selected that are not highly busy; rather, we focus on regions having lesser occupancy. This would have a positive impact on energy consumption profile of the processor. Algorithm 2 explains the details of this technique. The proposed algorithm is inspired from [39], which tries to optimize the global objectives based on a game theoretic approach. This process is explained in Figure 3. This figure shows that we are selecting a particular region for mapping various closely related tasks of the considered NN application. The tasks are the neurons of a particular neural network layer, and these tasks have to be mapped on a particular region. This technique will make sure that the tasks of a particular layer are processed first, followed by the next one. The technique will try to ensure that the communication latency is minimized at this level of mapping.
# Study on Effects of Energy Deposition and Thermal Shock Wave by Electron Beam Irradiation
## 4 Numerical Simulation of Radiant Thermal Shock Wave of Electron Beam
### Discussion of Calculation Results  
Table 3 shows the material parameters of carbon phenolic used in the calculation [32]. The strength of the thermal shock wave surpasses the strength of the material (C/P), so  
Figure 12: Energy deposition rates of different energy electrons in multilayer structures.  
the material is regarded as an ideal plastic body in the calculation. The energy deposition profile in carbon phenolic is shown in Figures 13 and 14.  
Figure 15 shows the spatial waveforms of thermal shock waves generated at different times under electron beam radiation in carbon phenolic with an average energy of 1.1 MeV and an incident energy density of \(F_{0}\) = 420 J/cm\({}^{2}\). Figure 11 shows the calculated results of stress peaks in carbon phenolic target plates under different energy radiation. Figure 12 shows the calculation results of stress peaks in carbon phenolic under the same incident energy of 1.1 MeV and different energy densities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(\rho_{0}\) (g/cm\({}^{3}\)) & 1.43 & \(Y_{0}\) (GPa) & 0.05 \\ \(c_{0}\) (m/s) & 3069 & \(E\)s (k/ g) & 5.14 \\ \(s\) & 1.578 & \(H\) & 0.5 \\ \(\Gamma_{0}\) & 0.24 & \(N\) & 7.59 \\ \(G\) (GPa) & 6.0 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters of carbon phenolic.  
Figure 14: Thermal shock wave attenuation in carbon phenolic.  
Figure 13: The spatial waveform of the thermal shock wave in carbon phenolic.  
Through calculation, it can be seen that the electron beam thermal shock wave in carbon phenolic has the following characteristics:  
(1) The peak value of radiant thermal shock wave is not at the radiation surface, but near the peak value of energy deposition.  
(2) The maximum thermal shock intensity occurs during the radiation process rather than at the end of the radiation.  
(3) Under the condition of constant energy density, the higher the energy of the electron beam, the higher the intensity of the thermal shock wave; when the energy of the electron beam is constant, the higher the energy density is, the stronger the thermal shock wave is. The intensity of thermal shock waves in carbon phenolics is the GPa order of magnitude under electron beam radiation with an energy density of 210-840 J/cm\({}^{2}\). The thermal shock intensity is 0.67-2.51 GPa at 5 mm behind the radiated surface.
## 5 Interference Distribution Characteristics
### Electrode Current  
The relationship between the polarization potential and current density is the same as in Section 5.1 and shown in Figure 7. The other parameters are listed in Table 1.  
The distribution of the leakage current density and pip-to-soil potential under different HVDC ground currents are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline HVDC grounding current & 100 A, 500 A, 1000 A, 2000 A, 5000 A \\ pipeline length & 100 km \\ distance between electrode and pipelines & 10 km \\ soil parameters & 100 \(\Omega\)-m, pH = 8 \\ pipeline steel material & X80 \\ pipeline coating resistivity & \(3.3\times 10^{7}\)\(\Omega\)-m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for the study model.  
Figure 9: Schematic diagram of the pipeline and HVDC grounding electrode arrangement.  
As can be seen in Figure 10, the leakage current density and PSP reach 3.92 A/m\({}^{2}\) and 0.89 V, respectively, when the grounding current is 5000 A. Both maximum leakage current density and maximum PSP increase as the electrode grounding current increases. Regression analysis shows that the relationship between the maximum leakage current density \(J_{\text{max}}\) and the electrode grounding current \(I_{\text{hvdc}}\) is linear, that is \(J_{\text{max}}=a_{1}\cdot h_{\text{hvdc}}+b_{1}\) where \(a_{1}\) and \(b_{1}\) are fitting constant coefficients.  
The maximum value is located at the midpoint of the pipeline. When currents flow out of the pipe, the pipeline faces corrosion risk, especially for the central area of the pipeline. Figure 11 shows the corrosion risk area distribution under different grounding currents.  
The length of anode area is 34.5 km under 100 A, whereas the length of anode area is 25.7 km under a 5000 A grounding current The result shows that the length facing corrosion risk decreases as the HVDC grounding current increases, which means the corrosion area is becoming more concentrated. The effect of grounding current on the anode area range is not very significant when the current exceeds 1000 A.
# AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection
## 3 The Proposed Method
### Assembling Text Components  
Benefitting from the parsimonious text component set, we specially design a straightforward approach for assembling positive text components into words/text lines, outputting a single enclosing rectangle for each word/text line. The function minAreaRect in OpenCV could be applied for this purpose. The following steps are applied to construct the final text lines.  
Firstly, we sort the text components according to their center points along the scanning direction. In ICDAR2013 dataset, the scanning direction is horizontal. Then, we take a currently un-assembled bounding box as the initial seed and form an initial text line TL. Third, TL iteratively expands by other un-assembled bounding boxes that are similar to the boxes in TL. This process is repeated until this TL no longer changes. Next, we select a new un-assembled bounding box as the new seed to deal with it in the above manner. Finally, when all the bounding boxes are assembled into their text lines, we inspect every pair of text lines; if there exist similar bounding boxes in different text lines, we merge these two text lines into a new one. The merging process repeats until no text line changes. Practically, we further slightly refine the side of the final text line in the horizontal direction according to the contour of the text mask.  
Note that one bounding box is considered similar to another bounding box only if (1) the aspect ratio of the two boxes is less than T and greater than \(1/T\); (2) the minimum horizontal distance between the two bounding boxes should be less than the average distance in the text line; (3) for orientation,  
Figure 5: The parsimonious text component set distinguished by the meaningful supervision of the text masks.  
the line connecting two bounding boxes should not deviate from the horizontal direction by an angle \(\theta\). In this paper, T is equal to 3. \(\theta\) is equal to 5 in the ICDAR2013 datasets and 90 in the MSRA-TD500 datasets.
## 5 Future Trends and Challenges
Therefore, social capabilities become the next barrier on the road to developing full self-driving cars to increase passenger safety, comfort, and efficiency, where intermediate steps from 2019 to 2027 will comprise awareness, sensing, cooperative, and synchronized driving [10]. As these skills become key for local decision-making (not only in vehicles but also in the interaction with infrastructure and pedestrians), new technological concepts and innovations must emerge. With this purpose, fog computing was introduced in 2014 by Cisco, and later promoted by the OpenFog Consortium, also joined by Dell, Intel, Microsoft, ARM, and Princeton University, as a technology to reduce the gap between cloud computing and IoT devices [16]. This new architecture allows things, apps, and devices to take advantage of the decentralization of the computing infrastructure and extend the services to the network border. Therefore, data, computation, and storage are distributed in the most logical and efficient place between data source and cloud. As a result, fog computing allows artificial intelligence (AI) to be brought to sensors. This paradigm, well-named as the Artificial Intelligence of Things (AIoT), will allow the performance of short-term analysis, which improves efficiency, reduces raw data traffic, and takes care of legal aspects about security and privacy (e.g., sensitive data subject to regulations in different countries). Popular fog computing applications include smart grid, smart city, smart buildings, software-defined networks, and vehicle networks. In particular, the benefits for vehicular fog computing (VFC) are large computation capacity, low latency, high mobility support, and low deployment cost. However, limitations in storage space, new computing architectures, and the operation of these systems in heterogeneous environments must still be investigated [144]. In relation to self-driving, future trends aim to protect the communications used in AVs against cyber-attacks to prevent the car occupants from suffering personal damage or data theft. To avoid potential security threats (e.g., such as in cybercrime-as-a-service), the automobile industry has set cybersecurity as a critical aspect to be included from the vehicle design to its decommissioning (i.e., to cover the full vehicle lifecycle) [145]. In order to achieve this goal, the car industry is currently involved in the development of the ISO/SAE 21434 standard to provide cybersecurity to road vehicles, which is expected to be published by 2020 [146]. Also with the goal of providing security to AVs, both Europe and the USA are promoting new strategies to include the auditing and certification of cybersecurity aspects beyond the own car functionalities. This includes validating the entire value chain from original equipment manufacturers (OEMs) and multi-tier suppliers to manufacturers [147]. Future steps should therefore be directed to consider the auditing and certification processes as an investment instead of a cost, also provided in the form of new industry approaches (e.g., automobile consortiums) to face the same security concerns [132].
## 4 Implementation  
A key criterion for selecting approaches for the proposed algorithm is ensuring computational feasibility for real-time implementation. To this end, the authors aimed to design algorithms that leverage parallel computation extensively, achieving significant performance improvement by utilizing GPU computations wherever possible, rather than relying solely on the CPU.  
For the curve approximation, each row of the depth image is approximated independently of the others. This characteristic allows the depth image to be divided into hundreds or even thousands of parallel computations, depending on its size. These computations can be assigned to the same GPU simultaneously, enabling efficient processing.  
Similarly, the marching cubes algorithm can evaluate and estimate cubes as separate entities, making it highly suitable for parallel computation on a GPU. This approach significantly accelerates the process compared to sequential CPU-based implementations.  
To implement the developed algorithm, the authors combined efficient C++ code with CUDA, taking full advantage of the GPU's parallel processing capabilities.  
Figure 12 represents the overall flowchart of the developed algorithm, and the hardware assigned for each task. It is shown that the majority of the computation is done on GPU alone and CPU is only used for transmitting data back and forth from system and GPU memory while needed. In this study, A laptop from Lambda, based in San Francisco, US, featuring an Intel Core i7-8750H processor, 32 GB of DDR4 RAM, and an Nvidia GeForce RTX 2070 Max-Q GPU was used to perform the implemented algorithm.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.1 Hybrid Approaches [238; 239]  
Combine rule-based and data-driven methods to balance efficiency and adaptability, particularly in scenarios with limited training data or RT constraints.
## 2 Optical Inspection Technologies in Railways
### Sensor Systems
#### 2.3.8 Clearance  
The measurement of the clearance is similar to the survey of tunnel structures. This involves measuring the clear space in order to keep the area free of obstacles and objects. It describes the vertical transverse plane of a track. MERMEC and TVEMA use the same measuring systems for both applications. Fraunhofer IPM offers additional measuring systems designed for clearance surveying. For example, two High-Speed Profilers (HSP) are installed on the LIMEZ III measurement train of Deutsche Bahn (see Figure 8), which can capture up to 1110 profiles per second. At a train speed of 100 km/h, this results in a profile distance of 25 mm [7; 43]. At the present time, the measurement technology is being converted to a new measurement train (Gleismestihebzug GMTZ) with simultaneous maintenance and updating of the infrastructure components of the measurement technology. The use of the data for Building Information Modeling (BIM) and infrastructure data acquisition will play an important role in the future.  
The Clearance Profile Scanner (CPS) from Fraunhofer IPM can detect up to 200 profiles per second [67]. At a vehicle speed of 50 km/h, this results in a profile distance of 70 mm, with more points per profile. The distance resolution is specified with 1 mm and uncertainty  
Figure 8: **(Left)** High-Speed Profiler (HSP) system from Fraunhofer IPM with two sensor heads mounted on the Deutsche Bahn inspection train LIMEZ III. (**Right**) Image of a point cloud captured by HSP.  
at an object is specified with about 3-7 mm. RIEGL offers the VMX-RAIL, a mobile laser scanner system developed for track and clearance surveying [44]. Three scanners with different orientations are combined. The system can record 750 profiles per second and is specified by the manufacturer for speeds up to 130 km/h. Optionally, six cameras can be installed with the system. The accuracy is specified with 5 mm and the resolution is specified with 3 mm at a 30 m range under RIEGL test conditions [44]. Zoller + Frohlich offers laser scanner specially for railway applications like clearance measurements. These scanners operate accurately at speeds up to 120 km/h [45]. The resolution of the range is specified as 0.1 mm with a range noise of 0.5 mm (Z + F PROFILER\({}^{\text{\textregistered}}\) 9012, Zoller & Frohlich GmbH, Wangen im Allgau, Germany).
# Climate Perspectives in the Intra-Americas Seas
## 2 Large-Scale Climate Forcing
### ITCZ  
The ITCZ is a near-equatorial band of intense rainfall that extends longitudinally and can easily be identified as a cloud band over the tropics. The ITCZ describes a meridional migration that responds to the Earth's orbit, as shown for January and July in Figure 1 (lower panel), when the ITCZ is located at its lowest and highest meridional positions, respectively. This seasonal migration is crucial as the meridional position modulates the distribution of rainfall over land and ocean. In addition to seasonal migration, the mean position north of the equator is a result of cross-equatorial heat transport from ocean circulation [71]. The position of the ITCZ depends on atmospheric energy balance, whereby small changes in the energy flux can lead to larger departures from its mean position [72]. The responsiveness of the ITCZ position to temperature changes is not restricted to the tropical domain. Changes at higher latitudes may also induce the asymmetric thermal forcing that leads to ITCZ displacements. The extratropical SSTs over the Southern Atlantic Ocean play an important role in the northern position of the ITCZ [73]. The ITCZ is connected with large-scale processes that modulate rainfall in several regions. Its impact over Africa is mostly associated with rainfall over the southern Sahara and northernmost Sahel [74]. For the South American climate, it is linked with Amazon rainfall through complex interactions with convectively coupled Kelvin waves [75]. During DJF, inter-hemispheric telecconnection is activated by vertical wind shear over tropical South America and the western South Atlantic [76]. This facilitates the enhancement of the ITCZ convection over northern South America. The connection with South American topography shows that the ITCZ southern branch feature has feedback with the Andes cordillera, modulating precipitation over the tropical Pacific [77]. Over Colombia, the ITCZ increases rainfall for one of the rainiest places on Earth--the Lloro [12]. For Central America, the ITCZ is a dominant feature of the annual hydrologic cycle and modulates moisture transport from the eastern tropical Pacific [78; 79]. Further interactions between the ITCZ and the CLLJ have been found to partially trigger a displacement of the ITCZ centre of mass, ultimately spreading the rainfall distribution [14]. Given the relevance of the ITCZ position for rainfall distribution, changes that may impinge variations in atmospheric energy balance are crucial to understanding how anomalous migrations of the ITCZ may affect rainfall. The influence of the ENSO is one of the most researched connections to the ITCZ anomalous meridional shifting. ENSO may increase the wind stress over the western equatorial Atlantic, modifying Atlantic equatorial ocean dynamics and providing a mechanism to couple rainfall pattern changes with ITCZ movements [80]. Over the IAS, the ENSO has been associated with a westward movement of the ITCZ, decreasing rainfall activity over the Pacific coast and increasing dry conditions.