esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study systematically analyzes reservoir formation mechanisms under deep burial conditions, integrating macroscopic observations from representative ultra-deep elastic reservoirs in four major sedimentary basins in central and western China. Developing effective clastic reservoirs in ultra-deep strata (6000-8000 m) remains a critical yet debated topic in petroleum geology. Recent advances in exploration techniques and geological understanding have challenged conventional views, confirming the presence of viable clastic reservoirs at such depths. Findings reveal that reservoir quality in ultra-deep strata is preserved and enhanced through the interplay of sedimentary, diagenetic, and tectonic processes. Key controlling factors include (1) high-energy depositional environments promoting primary porosity development, (2) proximity to hydrocarbon source rocks enabling multi-phase hydrocarbon charging, (3) overpressure and low geothermal gradients reducing cementation and compaction, and (4) late-stage tectonic fracturing that significantly improves permeability. Additionally, dissolution porosity and fracture networks formed during diagenetic and tectonic evolution collectively enhance reservoir potential. The identification of favorable reservoir zones under the sedimentation-diagenesis-tectonics model provides critical insights for future hydrocarbon exploration in ultra-deep clastic sequences.  
ultra-deep strata; clastic rocks; oil and gas reservoirs; formation mechanisms; hydrocarbon exploration potential +
Footnote †: journal: Applied sciences
###### Abstract  
Surface defects in magnetic tiles pose significant challenges to the performance and reliability of permanent magnet motors. Traditional defect detection methods, including visual inspection and 2D imaging, are limited by subjectivity, resolution constraints, and a lack of depth information, making precise defect quantification challenging. To address this challenge, this study explores a defect detection and quantitative evaluation framework based on high-resolution 3D laser scanning technology. Our approach integrates point cloud acquisition with luminance and point cloud mapping (LPM) to enhance defect visualization. Furthermore, we introduce an adaptive neighborhood selection method based on information entropy, enabling accurate normal vector and curvature estimation while reducing reliance on manual parameter tuning. Even when the point cloud density decreases to 40%, the mean estimation error and root-mean-square error remain within 3\({}^{\circ}\). By leveraging single-frame and multi-frame point cloud fitting, our method transitions from coarse defect extraction to fine refinement, enhancing detection precision. To further improve accuracy and minimize false negatives, we apply region-growing techniques for defect region completion. Experimental results indicate that our method can reliably detect surface defects as small as 0.07 mm\({}^{2}\), achieving an average precision of 93.91%, a recall of 95.97%, and an F1 of 94.91%. Compared to conventional 2D image-based methods, our method offers superior defect quantification, lower computational costs, and minimal hardware requirements, making it highly suitable for real-time online defect detection in industrial applications.  
magnetic tiles; tiny surface defects; 3D laser scanning; point cloud mapping; adaptive neighborhood selection; defect detection and quantitative evaluation +
Footnote †: journal: Applied sciences  
Article
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 3 Results and Analysis  
Experiments were performed on a Linux Ubuntu 20.04 operating system, with the CUDA 11.4 and the PyTorch 1.11 framework. The hardware configuration comprises an Intel Xeon Silver 4110 CPU and two NVIDIA RTX 2080 Ti GPUs, each equipped with 11 GB of dedicated memory.
## 3 Methods
### Lesion Perception Block  
The LPB is structured as shown in Figure 3, which primarily consists of multiple parallel convolutional layers with a kernel size of \(1\times 1\) and an LAH. The parallel convolutional layers are capable of extracting unique feature information for different types of lesions. The LAH utilizes different types of pooling layers to extract diverse feature information. It also introduces two consecutive \(1\times 1\) convolutional layers for channel contraction and expansion. A sigmoid activation function is inserted after the convolutional layers to introduce non-linearity and prevent gradient vanishing. Finally, the unique skip connection is used to enhance the perception ability of the network for subtle lesions.  
Specifically, the LPB performs element-wise addition on the output \(F_{i}^{MSAB}\) from the MSAB to generate \(F^{M}\). \(F^{M}\) is then passed through four parallel convolutional layers with a kernel size of \(1\times 1\) to reduce the channel dimension, resulting in four types of lesion features, \(FL_{l}\in R^{H\times W\times 3},l\in\{MA,SE,HE,EX\}\). Afterwards, \(FL_{l}\) is passed to LAH, where specific channel attention factors \(CH_{l}\), \(l\in\{MA,SE,HE,EX\}\) are generated to emphasize the unique channel information for each lesion type. The mathematical process of the LAH can be described as follows:  
\[CH_{l}=\sigma\bigg{\{}\mathcal{C}_{1}\left(FL_{l}^{avg}\right)_{\times 2} \bigg{\}}+s^{\prime}\sigma\bigg{\{}\mathcal{C}_{2}(FL_{l}^{max})_{\times 2} \bigg{\}},l\in\{MA,SE,HE,EX\} \tag{4}\]  
where \(\sigma(.)\) denotes the sigmoid activation function, \(s^{\prime}\) is the scaling factor, and \(FL_{l}^{avg}\) and \(FL_{l}^{max}\) represent global spatial features obtained through average pooling and max pooling.  
Figure 2: Multi-Scale Attention Block (MSAB).  
\(\mathcal{C}_{1}\) and \(\mathcal{C}_{2}\) are the \(1\times 1\) convolutional layer; \(\mathcal{C}(.)_{\times 2}\) denoting the operation is performed twice. Then, the LPB utilizes skip connections to perform element-wise multiplication between \(F^{M}\) and \(CH_{l}\), generating feature maps \(FR_{l}\) that contain rich lesion information. This can be represented as follows:  
\[FR_{l}=F^{M}\otimes CH_{l},l\in\{MA,SE,HE,EX\} \tag{5}\]  
At this point, the LPB completes the extraction of effective lesion information, especially subtle lesion information. Finally, \(FR_{l}\) is feedforward to the segmentation head containing a convolutional layer with a kernel size of \(1\times 1\) and a sigmoid function and then generates the segmentation results \(S\) for four types of lesions.
## Materials and Methods
### Micro Particle Image Velocimetry (\(\mu\)-Piv)  
In order to demonstrate the proof of concept an initial flow analysis is performed. The velocity field of the flow is determined using a Micro Stereo PIV system (\(\mu\)-PIV) 3D Stereo Flowmaster PIV/PTV-System  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aqueous Solutions** & \(n\) \\ \hline glycerol & 1.33–1.47 \\ zinc iodide & 1.33–1.62 \\ sodium iodide 60\% & 1.5 (60\% Nal by wt.) \\ potassium thiocyanate & 1.33–1.49 \\ ammonium thiocyanate & 1.33–1.50 \\ sodium thiocyanate & 1.33–1.48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Refractive index of fluids Budwig (2017).  
Figure 1: Molding process of a welded tube in schematic form: (**1**) welded tube; (**2**) molding and removing of the silicone core; (**3**) detailed silicone core; (**4**) fixing of the core in an aluminum square profile; (**5**) filling up the aluminum profile with a transparent silicone and removing of the core after curing; (**6**) final replicate for performing optical flow analysis.  
(LaVision, Gottingen, Germany) shown in Figure 2. Image processing and uncertainty analysis have been performed using the imaging software DaVis 8.4 (LaVision, Gottingen, Germany). The \(\mu\)-PIV in combination with a stereo microscope Discovery V20 by [PERSON] (b) (Oberkochen, Germany) allows for the analysis of simple and complex flows with high precision. For this, fluorescent particles, which follow the flow and make it visible for the cameras, are added to the test fluid. PMMA (polymethyl methacrylate)-RhB particles (LaVision, Munich, Germany) with a size of 10 microns are used.  
The particles are illuminated twice by a Nd:YAG double pulse laser (Litron Lasers, Rugby, UK) with a wavelength of 532 nm and recorded with sCMOS (scientific-grade Complementary Metal Oxide Semiconductor) double-frame cameras (a) Imager sCMOS (LaVision, Gottingen, Germany). The measurement setup (d) is placed under the microscope (b). The velocity vectors are calculated from the displacement of the particle between the two images using cross correlation technique.  
A schematic sketch of the measurement setup is depicted in Figure 3. The circuit consists of a micro-annular gear pump mzr(r)-7245 (HNP Mikrosysteme GmbH, Schwerin, Germany) with a membrane filter located in front and the sample. The membrane filter protects the pump from large agglomerates.  
Figure 3: Schematic sketch of the measurement setup.  
Figure 2: **Left:** (a) Micro Stereo particle image velocimetry (PIV) system consisting of double-frame cameras; (b) stereo microscope; (c) laser fiber and (d) measurement setup; **Right:** Measurement setup consisting of (e) micro-annular gear pump; (f) filter; (g) sample and (h) fluid container.  
The measurement was started 10 s after activating the pump, in order to ensure a fully developed flow. The experiments are carried out at 4000 rpm, corresponding to a flow rate of 192 mL/min. The pulse interval, which is the time between two laser pulses, is set to 400 \(\upmu\)s. In each case, 100 images are recorded and evaluated.
## 2 Materials and Methods
### Long Short-Term Memory (LSTM) Network Model  
A long short-term memory network (LSTM) is an improved cyclic neural network used to solve the problem that RNN networks cannot deal with long-distance dependence. [PERSON] proposed the LSTM algorithm [PERSON] (1992), which can store data information in a longer time step. Regarding the problem of time series prediction and analysis, LSTM can predict future data characteristics through the data characteristics of the past period time. LSTM networks enable nodes to \"remember\" or \"forget\" data through a \"gate\" structure, which mainly includes three \"gates\": the forgetting gate, the information adding a gate, and the information output gate. Through these three \"gates\", the input of each cell state contains the output of the previous moment, and the input of the current moment also contains some information stored by the node itself. Therefore, LSTM has a better performance on longer sequences. It is shown in Figure 4.  
Figure 4: LSTM model structure.  
Figure 3: The 1D-CNN model structure, the blue color parts represents processing dot product with input slide window part and kernel.  
The LSTM updates for time steps given inputs \(x_{t}\), \(h_{t-1}\), and \(C_{t-1}\), The operation is shown in Equations (4)-(9):  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[C_{t-1},h_{t-1},x_{t}]+b_{f}\Big{)} \tag{4}\]  
\[i_{t}=\sigma(W_{i}\cdot[C_{t-1},h_{t-1},x_{t}]+b_{i}) \tag{5}\]  
\[\widetilde{C}_{t}=\tanh(W_{\text{C}}\cdot[h_{t-1},x_{t}]+b_{\text{C}}) \tag{6}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{7}\]  
\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{8}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{9}\]  
where \(\sigma\) and \(\tanh\) represent the sigmoid activation function and hyperbolic tangent activation function, respectively; \(W\) and \(b\) represent the weight matrix and bias parameters, respectively; \(x_{t}\) represents the input of the LSTM unit at time \(t\); \(h_{t}\) represents the output of the unit corresponding to at time \(t\); and \(C_{t}\) represents the state unit of the LSTM at time \(t\). The whole LSTM unit includes three thresholds, namely forgetting gate \(f_{t}\), input gate \(i_{t}\), and output gate \(o_{t}\).
## 2 Materials and Methods
### Statistical Analysis  
The Kolmogorov-Smirnov test was used to test for data normality. Continuous normal variables were expressed as mean \(\pm\) standard deviation (SD), and continuous non normal variables were expressed as median, interquartile range (IQR). Dichotomous variables were expressed as counts and frequencies (n, %). To assess the differences between control and experimental groups, the Student's \(t\)-test or Mann-Whitney U-test was used for normally distributed and non-normally distributed variables, respectively. The paired samples \(t\)-test or the Wilcoxon test was performed to test pre- and post-intervention differences within groups, for normally distributed and not-normally distributed variables, respectively. Principal component analysis (PCA) was performed to identify body weight and body composition patterns. To choose the number of components to keep from the PCA analysis, the eigenvalues derived from the correlation matrix of the standardized variables were assessed, and the scree plot was checked for confirmation. Components with eigenvalue higher than one were retained for data analyses. Based on the concept that the component scores are interpreted as correlation coefficients (i.e., higher absolute scores indicate that the variable characterizes most to the component), the bodyweight and body composition patterns were defined in relation to variables that correlated most with the component (absolute factor loadings > 0.45). Moreover, the orthogonal varimax rotation was used to derive optimal patterns, which were non-correlated.  
Spearman coefficients were calculated in order to identify the factors that correlated mostly with % weight loss. Then, linear regression analysis was carried out to identify the factors that can \"predict\" weight loss after taking into account basic confounders. More particularly, the absolute % weight loss was set as dependent variable and several variables were set as independent ones, such as age, body weight patterns and blood exams. Sex-specific analysis was also performed. All analyses were carried out with the SPSS statistical software (Version 21.0, SPSS, Inc., IBM, Chicago, IL, USA). Statistical significance was set at \(p\)-value < 0.05.
## 4 Results
### Data Collection
#### 4.1.5 Content Creation by School Children
In a subsequent survey of the students, three challenges were identified: the necessary technical equipment such as internet-capable end devices and digital sound recording devices; the different prerequisites of the learners in terms of prior knowledge, digital skills, and working speed; and legal questions regarding the publication of the work results. The first solutions were the acquisition of the necessary technical equipment, flexible planning, sufficient staff to provide the learners with individual support, differentiated tasks, and obtaining written consent from the parents. The next steps of the project are to expand the focus of the content from local history to other historical topics, to provide in-depth training in digital skills for teachers and learners, and to expand the target group to include educators in both schools and museums.
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
## 3 Multi-Wavelength Path Loss Analysis and Modeling under Free Space Propagation  
This section will show path loss analysis and modeling under free space propagation with consideration of wavelength dependence. Firstly, the multi-wavelength path loss model in the single-LED scenario is proposed in the form of the ABG model. Secondly, the multi-wavelength path loss model in the multi-LED scenario is derived based on the proposed ABG model in the single-LED scenario. In our simulated scenario, the size of the indoor scenario is set as that of a typical conference: 5 m \(\times\) 5 m \(\times\) 3 m. The LED is all placed vertically downward in the center of the ceiling and the receiver is placed vertically upward on the floor in a uniform distribution. The other parameter configurations of the VLC system are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Scenario size & 5 m \(\times\) 5 m \(\times\) 3 m \\ LED Power in the single-LED scenario & 1 W \\ LED Power in the 3-LED scenario & 0.33 W \\ Semi-angle at half power & 60\({}^{\circ}\) \\ Mode number & 1 \\ Coating material & Plaster \\ Reflection coefficient & Wavelength-dependent \\ Length of reflecting elements & 0.1 m \\ Field of vision & 85\({}^{\circ}\) \\ Physical area of PD & 1 cm\({}^{2}\) \\ Transmission coefficient of the optical filter & 1 \\ Refractive index of len at PD & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The VLC system setup.
1. Intrinsic sources. The hydrochloric acid coming from the stomach of the patient itself generates the dissolution of the enamel due to its low pH, around 1. This occurs in people who suffer from gastroesophageal reflux, regurgitation, persistent vomiting and especially in patients suffering from eating disorders such as anorexia and bulimia [9; 10].
2. Extrinsic sources. These consist in exogenous acids from the consumption of acidic either solid or liquid foods such as fruits, fruit juices and carbonated beverages. They can also come from acidic medications such as ascorbic acid (vitamin C) or acetylsalicylic acid (aspirin(r)) [11; 12].
3. Idiopathic sources. These come from acids of unknown origin, where neither the anannensis nor the examination nor the complementary tests allow us to clarify the etiology of the observed lesion. The percentage of these cases is very small [8; 11].  
There is a strong correlation confirmed by several published studies between the consumption of solid foods and acidic liquids and dental erosion as a consequence of its chemical characteristics such as its low pH, its low buffering capacity and its low mineral content in calcium, phosphate and fluoride [9; 13].  
The regular intake of acidic foods such as fresh fruits and vegetables, pickled foods or vinaigrettes and saucs such as ketchup present a high erosive potential, determined above all by their pH below 4.55 [14].  
During the past decades, in certain Western societies, especially in America and Europe, there has been a significant increase in the consumption of non-alcoholic beverages, mainly juices and soft drinks [15; 16; 17; 18; 19; 20].  
This increase in the intake of juices and low pH carbonated beverages has been correlated with a significant increase in the prevalence of dental erosion. This prevalence varies in children from 56% to 85% of those who consume soft drinks daily; of whom 20% consume them four or more times per day [21].  
The frequency of intake of these drinks also influences the development of dental erosion. This statement is based on studies such as that of [PERSON] et al. (2011), who noted that patients who drank soft drinks during the whole week had more erosion than those who only consumed them during weekends, concluding that the higher the frequency of consumption, the greater the risk of suffering from the disease [22].  
In addition to the frequency factor, there are authors who consider the amount of ingested product as an important factor [22]. [PERSON] found in 14-year-old British children a high correlation between the amount of cola ingested and other soft drinks, apple juice and sports drinks, and the presence of dental erosion [22].  
The heterogeneity in the diagnosis and the variants that can produce erosion depending on the region in which they are studied make this review relevant in order to provide the pediatric dentist with a broad vision of which are the products that present a high erosive potential. Therefore, the objective of this study is to determine the prevalence and association of dental erosion and diet or beverages in children and teenagers.
# Power-Efficient Random Access Design for Machine Type Communication
## 5 Conclusions  
Machine type communication is a key component of Internet of Things for connecting devices. Cellular networks have the greatest potential to provide a reliable and global connectivity for MTC applications. One of the major challenges in cellular MTC is efficient random access, a process via which a device establishes a (wireless) communication link with the network. Its power and spectral efficiency is of particular importance to cellular MTC due to the MTC device's battery limitation and scarce cellular spectrum. In this paper, we review the random access mechanism in the traditional LTE/LTE-eMTC system and address its limitations when applied to MTC. Firstly, the bandwidth that is used for transmitting the random access preamble signal is fixed at 1.08 MHz, which prevents it from being deployed in a narrower bandwidth such as the 200 kHz re-farmed GSM bands, and hence hinders the application of legacy LTE (and LTE-eMTC) to mass IoT markets due to the already-scarce LTE bands. Secondly, the random access procedure that is optimized for human-type communications presents a significant overhead for machine-type data communications that are typically characterized by infrequent small data bursts. We then present a power- and spectrally-optimized, and bandwidth-scalable random access design, tailored specifically for cellular MTC. Specifically, we utilize a packet-based random access signal. We introduce the concept of effective bandwidth, and use it to optimize the power and spectral efficiency of the random access signal under a contention-based signaling environment. At last, the simulation shows that our proposed random access signal can achieve about 87% reduction in power consumption and 95.7% in resource usage comparing to the LTE/LTE-eMTC random access procedure.  
In the future, the number of access devices in the massive IoT scenario will be huge. In general, the random access delay increase along with the devices number increases. Fortunately, only a few of the devices are active at the same time. So one of the key challenges is to determine how to detect the active devices and then decode their data with low latency. Grant-free strategies might be a good solution to simplify the random access procedure and lower the access delay.  
Conceptualization, H.Y.; writing--original draft preparation, H.Y. and J.Z.; writing--review and editing, J.Z. and C.X.; supervision, J.Z.; validation, C.X. This research was funded by the National Natural Science Foundation of China (No. 61701234) and the Fundamental Research Funds for the Central Universities (No. 30917011318, No. 30920140122005). The authors declare no conflict of interest.
## 6 Experiments and Discussion
### Semi-Physical Simulation Experiments  
The CNR is difficult to be simulated without any personal bias, so STC, ATC and MATC are selected as the compared schemes to mainly verify the feasibility and effectiveness of the RMNCE-TC. The semi-physical experiment platform is shown in Figure 4. A trajectory generator is employed to produce the flight trajectory and corresponding true IMU data. Errors, such as bias, ARW, and VRW, are added to the true IMU data to simulate measured IMU data. Meanwhile, the barometer measurements are also simulated according to the true position data. All the sensor error settings can be found in Table 1. Furthermore, the Spirent GNSS simulator software suite SimGEN(tm) (Spirent Company, Sunnyvale, CA, USA) is employed to simulate GNSS data with 10 Hz output.  
Figure 4: Devices employed in the semi-physical simulation experiments.  
To evaluate the estimation accuracy of the RMNCE-TC algorithm, a time varying measurement noise deviation scheme was implemented. The standard deviation of the pseudo-range measurement noise was 1 m, except for: the period during the 730-th second to the 750-th second, where large errors were added to the satellites #10, #13, and #24 to simulate the multi-path effect; and the period during the 1900-th second to the 2500-th second, where all standard deviations were enlarged, particularly those of satellites #10, #13, and #24 were increased to 5m. The number of visible satellites and the detailed settings are listed in Table 2.