esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 2 Description of the Mechanism
### System Design
#### 2.1.2 Driving Subassembly  
The driving subassembly consists of two identical driving modules. Each module consists of a servo motor (double-winding), two identical spur gears, a trapezoidal screw, and an installation frame, as shown in Figure 6. The servo motor drives the spur gears, stretching the trapezoidal screw which is indirectly connected with the drogue, both of which are attached to the installation frame to form a module, and the trapezoidal screw  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Linear Directions**} & \multicolumn{3}{c}{**Angular Directions**} \\ \hline \(\mathbf{X}\) & \(\mathbf{Z}\) & \(\mathbf{X}\) & \(\mathbf{Y}\) & \(\mathbf{Z}\) \\ \hline \(\pm\)4 mm & \(\pm\)4 mm & \(\pm\)2.5\({}^{\circ}\) & \(\pm\)2.5\({}^{\circ}\) & \(\pm\)2.5\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tolerance range of the passive alignment subassembly.  
Figure 5: Principle of passive alignment subassembly: (**a**) slide along \(x\)-axis, (**b**) slide along \(z\)-axis, (**c**) rotate along \(x\)-axis (pitch), (**d**) rotate along \(z\)-axis (yaw), (**e**) rotate along \(y\)-axis (roll).  
locks the driving subassembly by its self-locking. Under normal conditions, the lower driving module works alone and drives the drogue to dock; as the backup, the upper driving module will work and drive lower module one to stretch by part connecting the two modules, thus ensuring the success of docking if the lower driving module does not work. This will increase the reliability of the driving subassembly by four times (as the servo motors are double-winding). The upper installation frame is the fixed end secured by the supporting subassembly. The properties of the driving subassembly are shown in Table 3.
## 2 Materials and Methods
### Effects of Surface Cluster Sizes  
To evaluate the effect of cluster sizes, the Si9H12-(NH\({}_{2}\))\({}_{2}\) and Si\({}_{15}\)H\({}_{18}\)-(NH\({}_{2}\))\({}_{2}\) surface clusters were used as one dimer and two dimers, respectively, of NH\({}_{2}\) passivated Si(100) substrates. The reaction pathways of TMG adsorbed on one dimer and two dimers are compared in Figure 1. The product 2 structures show that TMG is capable of being adsorbed within a dimer or between two neighboring dimers of the Si(100) surface by consuming two adsorption sites. The reactions proceeded according to the same trend. However, there was a noticeable difference in the first transition state, leading to the differentiation of activation barriers. The barrier at TS 1 of the one-dimer pathway was significantly less than for the two-dimers pathway (0.32 eV). In ALD simulations, the substrate model should yield consistent results with increasing system size. Thus, a one-dimer Si(100) model is inadequate for this study.  
As simulating the full Si(100) substrate is impractical, an optimal balance between model reliability and computational cost must be achieved. Accordingly, the most appropriate model is the smallest one that yields stable relative energy. To identify a suitable surface model, we simulated the adsorption of TMG on the two-, three-, and four-dimers of the Si(100) surface. They are compared in Figure 2. It was found that the reactions proceeded with the same trend. A significant differentiation in activation barriers was only observed for the one-dimer of the silicon substrate that might have been caused by steric effects. The smaller surface model allowed TMG molecules to approach closer to the surface than in the larger model during the adsorption stage. This led to an unrealistic small surface model (one-dimer). Therefore, the Si\({}_{15}\)H\({}_{18}\)-(NH\({}_{2}\))\({}_{2}\) surface cluster (two-dimers) was used as the substrate in this work.  
Figure 1: Reaction pathways of TMG adsorption on one dimer and two dimers of Si(100) substrates are illustrated by the red and black lines, respectively. The corresponding molecular structures are labeled (a)–(g) for the one-dimer pathway and (h)–(n) for the two-dimer pathway, as shown at the bottom of the figure.
## 4 Discussion
### Pain Situation and Quality of Life  
[PERSON] et al. [6] compared the efficacy of knee joint distraction (n = 23) and HTO (n = 46). In line with our results, these authors found significant improvements for the HTO group in the VAS pain score (\(p\) = 0.006) and physical component of SF 36 (\(p\) = 0.024) one year after surgery. [PERSON] et al. [13] evaluated return to sports and quality of life after HTO in athletic patients less than 60 years of age. The patients had the same age (mean age: 55.6 years) as our patients (mean age: 55.3 years) and in line with our results, they observed a significant improvement (\(p\) = 0.01) in quality of life pre-operatively (65%) compared with one year postoperatively (73%). In this time interval, the SF-36 physical sub-score also increased significantly (\(p\) = 0.02) from 59 (55-63) to 67 (58-72). However, the level of quality of life was consistently higher than in our investigation (from 59 to 67 vs. 30 to 45). However, this comparison is only valid for the preoperative evaluation because the postoperative examination is (still) different (one-year vs. six-months). [PERSON] et al. [7] reported a similar level of quality of life (SF-12 physical score: 53.6 \(\pm\) 9.7) for a non-selected population of HTO patients (closing-wedge HTO: n = 88; opening-wedge HTO: n = 51) as in our study to exam 4 (45.0 \(\pm\) 6.36). [PERSON] et al. [34] examined the health-related quality of life (assessment: SF-36) in patients (n = 120) after HTO. In line with our study, they also found significantly longitudinal (after 6, 12 and 18 months postoperatively) improvements in the physical score (\(p\) < 0.001) in contrast to small changes in the mental score of the SF-36 (\(p\) = 0.360). The amount of improvement (13.6 vs. 9.3) over the time (six months) in the physical score was much higher in our study compared those of [PERSON] et al. [34]. [PERSON] et al. [47] also observed a significant increase in health related quality of life (SF-36) and decrease in pain (VAS; \(p\) < 0.001; \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.4) among patients (n = 64) after open-wedge high tibial osteotomy across the same time period of six months. The improvements in the physical score (\(p\) < 0.001; \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.4) of the SF-36 were also much higher than in the mental score (\(p\) = 0.008, \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.1). [PERSON] and [PERSON] [48] reported an increase in the physical scores of patients (n = 414) with knee OA before surgery and at a minimum of 12 months following knee replacement (33.6 to 45.6). In contrast, the change regarding the mental score was clearly smaller (52.2 to 55.1). We hope that our future investigations (one year and two years postoperative) will provide further insight into the long-term outcomes after HTO and rehabilitation. In summary, the HTO and the subsequent rehabilitation led to a significant improvement of the physical component of the quality of life. However, the mental part of the quality of life remained unaffected.
## 4 Inland and Marine MLH Detections Using Ground-Based CDWL
### MLH Diurnal Cycles at the Inland and Marine Sites  
Figure 3 shows the two-day ground-based CDWL measurement results and MLH retrieval result. Figure 3a-h show the results at the inland sites (Inner Mongolia and Hefei). The CDWL measurement results include horizontal wind speed, horizontal wind direction, vertical wind speed, and TKEDR. The MLH retrieval result is MLH\({}_{\text{TKEDR}}\), which is drawn as pink circle above the TKEDR in Figure 3d,h. There are two significant diurnal cycles in both the TKEDR and MLH. This diurnal cycle is caused by the indirect forcing of solar radiation via the transport process between the ground and atmosphere [1]. Turbulence is one of the important transport processes. After sunrise, the ground surface temperature increases rapidly. The heat is then transmitted to the atmosphere, resulting in an unstable atmosphere stratification. It promotes the generation and development of convective turbulence, and the turbulence intensity reaches its peak at about 1400 LST. The convection is also can be seen in Figure 3c,g with the violent vertical wind. After sunset, the ground surface temperature is gradually lower than the atmosphere temperature due to surface radiative cooling, resulting in a stable stratification with weak turbulence. The MLH diurnal cycle  
Figure 2: **(a)** CNR, **(b)** normalized range-corrected CNR and its **(c)** HWCT, **(d)** lg(TKEDR) profiles at 1202 LST on 27 September 2019 in Inner Mongolia. **(e–h)** is at 1208 LST on 12 May 2019 in Hefei. **(i–l)** is at 1209 LST on 10 November 2018 in Sansha. Blue circle and pink circle denote the MLHs retrieved from CNR and TKEDR, respectively.  
varies with the turbulence diurnal cycle because the MLH is defined by the turbulence intensity. Additionally, the MLH maximum is at about 1400 LST.  
The results at the marine site (Sansha) are shown in Figure 3i-1. It is worth noting that compared to Inner Mongolia and Hefei, the convective vertical wind and turbulence are not violent, as shown in Figure 3k,l. Additionally, the diurnal cycle in the MLH is not significant, as shown in Figure 3l. In addition, the MLH retrieval result is distributed mostly over 0.5-1 km. A reasonable explanation is that the sea surface temperature varies slowly due to the large volume and high heat capacity of seawater, thus reducing the impact of solar radiation on the turbulence diurnal cycle. To provide a reference, the MLH\({}_{\text{RH}}\) retrieved from the RH profile measured by the radiosonde is given [16], which is drawn as a black diamond above the TKEDR in Figure 3l. The MLH\({}_{\text{RH}}\) shows good agreement with the MLH\({}_{\text{TKEDR}}\), validating that the constant TKEDR threshold determined in Section 3 is reliable.
## 5 Enhancement of Dental Imaging Protocols and Applications
### AI Integration and Machine Learning (ML) in Dental Imaging  
AI, including deep learning (DL) and machine learning (ML), has become a basis in the advancement of dental imaging. These technologies automate diagnostic processes, improve image interpretation, and support clinical decision-making in both routine care and complex surgical planning [2; 23].  
Modern AI models demonstrate superior performance in image classification, object detection, and segmentation, enabling accurate identification of dental caries, periodontal disease, root morphology, and anatomical landmarks essential for surgical navigation. Byintegrating multimodal data, such as CBCT scans with intraoral imaging, AI can generate comprehensive 3D reconstructions. These models enhance personalized treatment planning and preoperative simulations [24].  
Commonly used deep learning architectures such as U-Net and ResNet have shown promising accuracy (>90%) in caries detection and landmark identification in CBCT datasets. However, many models lack external validation. For example, a study using U-Net for periapical lesion detection reported an AUC of 0.93, but was limited to a dataset from a single institution, raising concerns about generalizability [25]. Furthermore, when comparing across dental specialties, AI performance tends to be more robust in visualized tasks such as caries detection and landmark identification, while it is less validated in areas requiring contextual judgment, such as endodontic retreatment planning or TMJ pathology [24,25].  
ML algorithms further elevate diagnostic precision through predictive modeling and longitudinal analysis. Clinical applications include early detection of bone loss, monitoring lesion progression, and identifying anatomical anomalies. In addition, radiomics, the extraction of high-dimensional features from medical images, reveals subtle, subvisual patterns linked to disease phenotypes. When combined with DL models, radiomics significantly enhances diagnostic sensitivity, particularly in areas such as maxillofacial oncology and endodontics [26, 27, 28].  
Generative AI, particularly Generative Adversarial Networks (GANs), is playing an increasingly important role in dental imaging. These models enhance image quality by removing noise, reducing artifacts, and improving resolution. Additionally, GANs can generate synthetic training data, which is especially valuable for improving algorithm performance in detecting rare or underrepresented pathologies. Such capabilities also support scalable applications in dental education and tele-denistry platforms [2].  
Beyond diagnostics, AI contributes to clinical decision-support systems by analyzing large-scale imaging and clinical datasets to propose individualized treatment strategies. Applications include optimizing surgical guide design, predicting post-operative outcomes, and reducing intraoperative risk. The integration of AI and ML in dental imaging indicates a progression toward more accurate, efficient, and personalized patient care [29].
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 1 Introduction
Generally speaking, existing works for pedestrian trajectory prediction could be mainly classified into distance-based methods [6; 7; 14], attention-based methods [10; 15; 16; 17], and graph-based methods [8; 9; 18; 19]. According to the analysis above, these methods have shortcomings in effectively modeling social interactions and temporal dependencies. To solve the problems mentioned above, we delved into non-autoregressive pedestrian trajectory prediction with transformers to propose a Non-autoregressive Sparse Transformer (NaST) network. Our work is in line with recent approaches [24; 27; 28]. In order to capture the spatial interaction, we designed a sparse spatial transformer. Instead of making dense interactions between a particular pedestrian with all of the others in a scene, we decided to let the model discover the exact neighbors who were involved in the interaction and learned the proper weights for them. More specifically, the spatial transformer first computed weights for each agent by multi-head self-attention. Then, the attention weights were filtered by a designed sparse interaction mask to prune the irrelevant neighbors. Finally, the remaining ones were aggregated to compute the spatial interaction. In addition, the spatial interaction was directed since the influence of social interaction was not identical to each other between pedestrian pairs. Therefore, the sparse spatial transformer can learn adaptive self-attention and find the exact set of pedestrians involved in social interactions. In the same way, the pedestrian motion feature is captured by a sparse temporal transformer. In particular, the sparse spatial transformer and sparse temporal transformer are jointly combined to model the spatial social interactions and temporal motions. Furthermore, we designed the transformer in a non-autoregressive way to predict all of the future locations at one time, which could avoid error accumulation and would be less computationally intensive. Finally, our proposed NaST is evaluated on the commonly used ETH [29] and UCY [30] pedestrian trajectory prediction datasets. The experiment results indicated that our method outperformed some of the state-of-the-art methods. Compared with the existing methods, this is the first proposal of sparse transformers and the application of non-autoregressive inference in trajectory prediction tasks. In summary, our contributions are three-fold: (1) We propose a Non-autoregressive Sparse transformer network for pedestrian trajectory prediction tasks. The advantage of the model is demonstrated by the experiments. (2) We propose a model of social interaction between pedestrians with a sparse spatial transformer encoder and extract the temporal dependencies with a sparse temporal transformer encoder. (3) We investigate the use of non-autoregressive inference with a transformer neural network to avoid error propagation.
# Central and Periodic Multi-Scale Discrete Radon Transforms
## 6 Example of Central DRT Application  
A suitable problem for demonstrating the capacity of central DRT transform is the automatic localization of rectangles on images taken with cameras of smart devices. In this context, the use of central DRT provides an important speedup (at the cost of omitting some cornered lines).
## 3 Methodology
### Feature Selection
#### 3.3.1 Information Gain  
The concept behind information gain (also known as entropy) measures the bits of information available for class prediction. Given a single attribute, each value will be evaluated through Equation (6):  
\[E(v)=-(P(2)\text{log}_{2}P(2)+P(1)\text{log}_{2}P(1)+P(0)\text{log}_{2}P(0)) \tag{6}\]  
where \(P(2)\) denotes the probability of class 2 occurring with the attribute value, \(P(1)\) indicates the probability of class 1 occurrence, and \(P(0)\) indicates the probability of class 0 occurrence. Given these values, the expected new entropy can be calculated through Equation (7):  
\[E_{new}(v)=\sum P(v)*E(v) \tag{7}\]  
where \(P(v)\) denotes the probability of the value \(v\) occurring, and \(E(v)\) indicates the entropy for this value. Then, the information gain using Equation (8) will be:  
\[I(v)=E(v)-E_{new}(v) \tag{8}\]  
Original entropy is simply the entropy using the probability of each target class occurrence. Given that the original entropy of the data remains static, the smaller the expected entropy value, the larger the information gain. In the context of feature selection, a feature with the lowest expected entropy will be seen as the most valuable by this measure.
# Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review
## 2 Review of Reflection Seismology
The pre-stack domain consists of seismic traces gathered by their common point of reflection or CMP. By performing velocity analysis, the seismic data can be transformed into the post-stack domain, which emulates a zero-offset section obtained using a linear source (or a planar source for 3D experiments). Generally, velocities are estimated by picking the maxima of a semblance function computed across hyperbolic trajectories on selected CMP gathers [22]. Finally, migration [19] is a fundamental step to overcome the assumption of horizontally stratified earth of the CMP method, which obviously does not work in the presence of dipping structures. Migration is a computationally expensive process that allows repositioning dipping reflected events to their correct spatial and temporal locations, thus removing the distortions present in the stack image in case of non-horizontal features. Migration is a fundamental process in seismic data processing that involves relocating seismic events to their true positions in the subsurface [2,19]. The technique removes the effects of wave propagation from the recorded seismic data to correct the spatial positioningof reflection events, thereby increasing the lateral resolution of the resulting image [23]. The primary goal of migration is to produce a stacked section or volume that resembles a geologic cross-section. At the end of the processing phase, 2D or 3D images are available to be used in various fields of geological sciences and applications. However, these images need an interpretation, as they are a geophysical representation of the subsurface; therefore, they are subject to several limitations and uncertainties, as discussed by [PERSON] and [PERSON], [2]. For example, reflectivity can be affected by a wide range of factors, such as the heterogeneity in the distribution velocity and density of the subsurface materials, the geometry of the reflectors, presence of fluids, presence of noise and other artifacts in the data. Different types of noise can make it difficult to distinguish between real geological features and artifacts. For example, multiple reflections from shallow high-impedance interfaces, which could not be entirely removed during the processing phase, may obscure deeper reflectors, or they can be misinterpreted as true reflectors. Similarly, the presence of gas or other fluids in the subsurface can modify the reflectivity or deteriorate the seismic image. To overcome these challenges, a variety of interpretive techniques have been developed to analyze seismic reflection data and extract meaningful information about the subsurface (see [PERSON], [10]). These techniques can include mere visualization tools as well as more quantitative methods such as waveform inversion and seismic attribute analysis that will be discussed in the following parts.
# Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
## 3 Methodology for Tunnel Geology Prediction Based on Instrumented Drilling Tests
### Deep Neural Network  
A deep neural network can implement functions of higher complexity than a shallow neural network when the same number of resources is used [25]. The difference between a basic neural network and a deep neural network lies in the depth and complexity of the model. This complexity is attributed to the elaborate patterns of information flow throughout the model. Because of the complexity of the relationships between rock properties and drilling test data, the use of a deep neural network may enhance predictive accuracy. The deep neural network model used to train the drilling test data is shown in Figure 4. There were nine input parameters: drilling speed, torque, rotation speed, propulsion, blow energy, blow number, water supply rate, water supply pressure, and EV energy data. In the normalization layer, batch normalization was used to normalize the input data for each batch and to normalize the distribution of data so that the mean of the data would approximate 0 and the standard deviation of the data would approximate 1. This approach enables higher learning rates, and improves the generalization accuracy [26]. There were three fully connected hidden layers, with each weight connected to all the outputs of the previous layer. The information flow was as follows. First, in order to reduce overfitting, two fully connected hidden layers randomly drop out the processing units in each epoch, where one epoch is defined as one pass of the entire dataset through the neural network. Overfitting occurs when the prediction result corresponds too closely to a particular set of data and fails to fit additional data. Overfitting can be determined through comparison with the training accuracy and validation accuracy curve, as shown in Figure 5. If the gap between the training accuracy and the validation accuracy curve is large, then overfitting has occurred. The output layers used the SoftMax activation function [27] to output the predicted value.  
Figure 4: Deep neural network model (\(X\): input data, \(y\): predicted probabilities of each value).  
In the fields of speech recognition, computer vision, and natural language processing, deep neural networks have already delivered significant improvements. This progress notwithstanding, deep neural networks often require years of experience to effectively choose optimal hyperparameters. The hyperparameters of the deep neural network, such as the number of hidden layer processing units, the choice of activation function for each hidden layer, and the loss function for the output value for geology prediction, are difficult to select empirically but which can be obtained using optimization algorithms.