esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Conclusions of the Literature Review  
CMMs have been used in the warm forming industry due to their high measurement precision [5]. However, they are not suitable for in-line processes due to (1) the high temperatures of the workpiece affecting or even damaging the measuring probe and, (2) the long measurement times of the probe even for cooled-down workpieces. Other alternatives for dimensional inspection include optical scanner technologies, which do not rely on contact with the workpiece. However, the radiation due to the high temperatures can affect the data acquired by the scanners [19]. Current literature for in-line dimensional inspection of warm workpieces is very limited [1] and accounts only for very simple geometries (such as cylindrical workpieces) [20; 21; 23; 24].  
Responding to the current state of the art, we present an optical system for in-line dimensional assessment of forged still-warm workpieces. Whereas previous cold-state methods would require approximately 20 min for the assessment of a single workpiece, our system spends less than 60 s per part. In-line assessment of these warm (approx. 600 \({}^{\circ}\)C) workpieces fills a gap in manufacturing processes in which early detection of an inherent planning, design, or manufacturing error is more important than the higher precision obtained with standard cold-state measurement methods. The system is implemented and deployed in a global automotive part maker plant, where the number of defective workpieces is reduced by a 95% with respect to previous dimensional assessment methods (i.e., cold-state CMM).  
The implemented system executes 3D scanning, mesh registration and comparison (against a CAD database) of the geometry of a forged still-warm workpiece. The system is capable of in-line measurements of circular runout of revolution warm workpieces, singled out from the forming process. Contrast test against cold-state CMM measurements show that the warm-workpiece measurement is good enough for the manufacturing plant in which the system is deployed (error below 0.1 mm for parts as long as 160 mm). The temperature-vs.-runout analysis shows no correlation between these two variables at such level of uncertainty. A prior-to-operation ANOVA test with cold workpieces validates the repeatability and reproducibility (R&R) of our measurement system.
# Modeling of Transmission Compliance and Hysteresis Considering Degradation in a Harmonic Drive
## 2 Mechanical Model
### Ideal Model  
Considering the different working modes for HDs, the ideal kinematic and mechanical relationships of these three ports, as explained in [11], can be expressed as follows:  
\[\theta_{WG}=(N+1)\theta_{CS}-N\theta_{FS} \tag{1}\]  
\[T_{WG}=\frac{1}{N+1}T_{CS}=-\frac{1}{N}T_{FS} \tag{2}\]  
where \(\theta_{WG}\), \(\theta_{CS}\) and \(\theta_{FS}\) denote the angular position of WG, CS, and FS, respectively; \(T_{WG}\), \(T_{CS}\) and \(T_{FS}\) denote the corresponding torques; and \(N\) is the gear-reduction ratio, which can be described as:  
\[N=\frac{Z_{FS}}{Z_{CS}-Z_{FS}} \tag{3}\]  
where \(Z_{CS}\) and \(Z_{FS}\) denote the number of teeth on the CS and FS, respectively. The most common configuration of an HD is illustrated in Figure 2: a fixed-member CS; a driven-member FS; and a driving-member WG. The basic harmonic-drive equations that capture this three-port behavior are:  
\[\theta_{WG}=-N\theta_{FS} \tag{4}\]  
\[T_{WG}=-\frac{1}{N}T_{FS} \tag{5}\]  
However, the above equations describe HD behavior under the assumption of an ideal linear input/output relationship, in which friction, torsional compliance, and kinematic error effects are ignored. These factors should be considered to obtain a better result [15].
## 3 Experiments  
### Dataset  
The mini-MIAS dataset is a publicly available database of mammograms that was developed for research purposes. It contains a total of 322 images, with each image corresponding to a single breast. The images were captured at a resolution of 1024 \(\times\) 1024 pixels and are presented in grayscale. The dataset includes images of both normal and abnormal breasts, with abnormalities including masses, calcifications, and architectural distortions [7; 51; 52].  
In our study on breast tumor segmentation, we utilized the mini-MIAS dataset to train and evaluate our model. We split the dataset into three subsets: 70% for training, 15% for validation, and 15% for testing. The training subset was used to optimize the model parameters and to train the model using data augmentation techniques. The validation subset was used to monitor the performance of the model during training and to select the best model based on the validation loss. The testing subset was used to evaluate the performance of the final model on unseen data.  
We applied several data augmentation techniques to the training subset. These techniques were applied randomly to each image during training, effectively creating new examples that were slightly different from the original ones. Data augmentation was not applied to the validation or testing subsets, to ensure that the model's performance was evaluated on unseen data.  
The mini-MIAS dataset has several advantages for research in breast cancer diagnosis and treatment. It contains a relatively large number of images that cover a variety of abnormalities, making it useful for training and evaluating deep learning models. The images are also annotated with ground truth segmentation masks, which makes it possible to evaluate the performance of segmentation algorithms. Additionally, the dataset is freely available to researchers, which helps to promote collaboration and reproducibility in the field [47; 53].
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 2 Learning-Based Approaches
### Keypoint-Based Approaches  
Keypoint-based approaches establish 2D-3D correspondences between images and then measure the pose according to these correspondences [28; 29; 30]. The procedures for keypoint-based approaches can be divided into two steps: 1. extract the 2D feature points in the input image; and 2. regress the 6D pose results using a PnP algorithm. [PERSON] [31] leveraged CNN to predict the 2D projections of eight vertices of the 3D bounding box of the object (Figure 5). To solve the challenges presented by textureless symmetrical objects,  
Figure 3: Architecture of the CNN predicting the projections of the control points.  
BB8 restricted the range of the rotation angle of the training data and used a classifier to predict the rotation angle during the estimation step. However, when the object is partially invisible, BB8 may not obtain the correct 3D bounding box, which would have an adverse influence on PnP. To solve this problem, [PERSON] [32] et al. proposed a method that segmented the image into several patches and made them predict both to which object they belonged and where the 2D projections were. Then, all the patches belonging to the same objects would be combined to measure the 6D pose based on PnP. Because each patch of the object is used to measure their respective local pose, this method was able to perform well when faced with occlusion. PVNet [33] predicted the direction of each pixel to each keypoint; thus, the spatial probability distribution of 2D key points can be obtained in a manner like RANSAC. According to the distribution, uncertainty-driven PnP could be used to measure the 6D pose. Predicting the direction of pixels and keypoints makes the local features more prominent. Even if one feature point is invisible, it can be positioned by means of another visible part. [PERSON] et al. [34] proposed a method involving learning orientation-induced primitives, rather than employing 3D bounding boxes, and calculated the rotation and translation vector in different modules. The methods mentioned above are all two-step-based; however, [PERSON] [35] revealed the weakness of this kind of method. First, it is not an end-to-end system. Additionally, the loss function of the neural network cannot represent the accuracy of 6D pose estimation. Therefore, [PERSON] et al. proposed a single-stage 6D pose estimation method that could directly regress the 6D pose on the basis of groups of 3D-to-2D correspondences associated with each 3D object keypoint.  
Figure 4: Symmetrical objects.  
Figure 5: The red bounding boxes for the pose estimation results using BB8.
## 6 Conclusions and Future Work  
For the two challenges that face the autonomy of AUVs, i.e., localization and ocean currents, this paper proposes a systematic approach. Firstly, the localization problem is solved with the aid of three surface buoys. By leveraging the different latencies of the three surface signals arriving at the AUV, the position of AUV can be calculated every localization cycle. The acoustically calculated position can then be utilized to correct the predicted and possible inaccurate position in dead-reckoning with EKF. Moreover, the uncertain ocean currents and full state reconstruction can also be incorporated in the framework of EKF. An MPC controller is designed to handle system constraints and control metrics based on the estimated full states from EKF. Simulation results demonstrate that the proposed integrated acoustic localization and tracking control approach outperforms the tracking control without acoustic localization. The potential of applying the proposed algorithm to AUVs is illustrated especially in addressing the challenges of underwater localization and ocean currents. Future research will consider extending the AUV dynamics to three dimensional space and the possibility of cooperative localization and control with multiple AUVs.  
Conceptualization, W.X.; methodology, D.Z. and H.Z.; software, D.Z.; validation, H.Z.; formal analysis, D.Z.; investigation, D.Z.; writing--original draft preparation, D.Z.; writing--review and editing, W.X.; visualization, H.Z.; supervision, W.X. and H.Z.; project administration, W.X.; funding acquisition, W.X. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China under Grant 61773343, the National Key Research and Development Program of China under Grant 2017 YFC0305900, and the Research Funds from Shenzhen Investment Holdings Company Limited.  
Data sharing not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Experiments and Results
### Result Analysis and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Models**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Convolution**} & \multicolumn{2}{c}{**Transformer**} & \multicolumn{2}{c}{**Hybrid**} & \\ \cline{2-9}
**Class** & **ResNet50** & **EfficientNet** & **ViT** & **Swin** & **CvT** & **CoAtNet** & **WMP** & **Proposed** \\ \hline Bog & 90.14\% & 89.07\% & 81.94\% & 88.87\% & 90.71\% & 92.15\% & 88.15\% & **94.79\%** \\ Fen & 83.29\% & 82.61\% & 78.14\% & 81.19\% & 84.63\% & 86.85\% & 82.45\% & **90.57\%** \\ Swamp & 80.66\% & 79.81\% & 73.93\% & 75.56\% & 79.01\% & 84.47\% & 81.37\% & **89.04\%** \\ Marsh & 75.80\% & 79.42\% & 79.59\% & 76.91\% & 84.94\% & 89.52\% & 82.03\% & **89.91\%** \\ Water & 98.66\% & 98.62\% & 98.97\% & 98.63\% & 99.07\% & 99.21\% & **99.43\%** & 99.02\% \\ Forests & 90.52\% & 90.34\% & 87.32\% & 89.20\% & 90.94\% & 94.29\% & 91.09\% & **95.96\%** \\ Shrutlands & 88.35\% & 87.61\% & 84.46\% & 85.56\% & 91.29\% & **96.66\%** & 88.18\% & 94.88\% \\ Grassland & 82.86\% & 82.77\% & 78.15\% & 79.45\% & 83.29\% & 89.75\% & 85.89\% & **93.41\%** \\ Pastures & 93.89\% & 94.93\% & 92.23\% & 91.71\% & 93.74\% & 95.40\% & 96.33\% & **98.08\%** \\ Barren & 92.33\% & 89.85\% & 92.35\% & 86.20\% & 82.03\% & **98.66\%** & 94.63\% & 94.23\% \\ Urban & 98.77\% & 98.32\% & 98.90\% & 97.90\% & 98.44\% & 99.21\% & 99.13\% & **99.46\%** \\ \hline OA & 90.39\% & 90.18\% & 87.05\% & 88.75\% & 90.82\% & 93.34\% & 90.91\% & **95.36\%** \\ AA & 88.66\% & 88.49\% & 86.00\% & 86.47\% & 88.92\% & 93.29\% & 88.84\% & **94.49\%** \\ k & 89.08\% & 88.82\% & 85.26\% & 87.22\% & 89.56\% & 92.44\% & 89.64\% & **94.72\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Per-class and overall accuracy metrics of the deployed classification models for wetland classification (the highest accuracies per class and overall metric are shown in bold). both convolutional and transformer blocks improved the classification performance of our hybrid model by capturing both local and global patterns. Additionally, the use of the LFFN module helps to better preserve spatial features within different blocks of the model. Overall, the accuracy metrics support the visual interpretation of our findings, highlighting the superior performance of the Wet-ConViT model for classifying wetlands.
## 5 Model Training and Result Discussion
### Comparative Test of Model Detection Effect Based on Voting Method  
To verify the effectiveness of the model, in this section a comparative test was conducted between the CRNN model based on time series data and the traditional CNN based on single-frame images. All video data were collected based on existing cameras at the UUT site, uploaded to the data center through the local area network for BasicVSR++ and sliding window-frame extraction, and finally analyzed. The sliding window hyperparameter of the CRNN model was 10, and it was trained based on the video data; the CNN model was trained based on the image data converted from the same training datasets and tested on the same test datasets. To ensure the fairness of the comparison, each time the CNN was given 10 video frames to measure its detection effect. The traditional CNN model judged each frame of the video data separately, and finally used the voting method to take the category with the highest votes as the leakage level of the video. The accuracy of the CRNN on the test set reached 0.9852, while the performance of the CNN on the test set was only 0.96, which also proves that the developed detection method had a high detection ability. In order to intuitively compare the differences between the two models in dealing with this problem, a set of data was randomly selected for visualization and analysis. As shown in Figure 14, this set of video had a total of 10 frames, and the leakage level was 2.  
The CNN model makes a grading judgment for each frame of the picture, where the first 6 frames are judged as leakage level 1, and the last 4 frames are judged as leakage level 2. According to the voting method, the category with the highest votes is used as the leakage level of the video, so the video is judged to be leakage level 1, which is an incorrect judgment. The CRNN model based on time series information directly judges the video data as leakage level 2, which is a correct judgment. The CRNN model based on a time series can extract trend and periodic features in the image, and it can ignore the influence of the external environment on the image features, bringing higher accuracy and robustness.  
Figure 13: Confusion matrix of the dataset of 10 frames per sample on the test set: (**a**) difference between the predicted results and the true labels of various leak-age levels; (**b**) proportion of predicted results to true results.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 2 Fundamental Processes
### Roles of Oxygen and Hydrogen Peroxide
In a SCWO unit without a preheater, using O\({}_{2}\)-H\({}_{2}\)O\({}_{2}\) as the oxidant may have a faster reaction rate, because there was no mass transfer resistance between H\({}_{2}\)O\({}_{2}\) and the organic material, and the reaction may start before the supercritical condition. To enhance the oxidation effect and reduce the operating cost as much as possible, O\({}_{2}\)-H\({}_{2}\)O in supercritical conditions was used as the main oxidant and mixed with organics and a small amount of H\({}_{2}\)O\({}_{2}\) at the reactor entrance. Since the addition of small amounts of H\({}_{2}\)O\({}_{2}\) significantly enhanced the initial oxidation rates and O\({}_{2}\) mainly played a role in the later stage of the reaction, the combination of H\({}_{2}\)O\({}_{2}\) and O\({}_{2}\) reduced reaction time and operating costs. In addition to the heating mode of the reaction, the different effects of O\({}_{2}\) and H\({}_{2}\)O\({}_{2}\) may also be related to the structural properties of organic compounds. [PERSON] et al. [79] simulated the reaction process by [PERSON] and found the accumulation of HO\({}_{2}\bullet\) and HO\(\bullet\) in the initial reaction of acetic acid's SCWO. Further, they inferred that the decarboxylation of acetic acid could not be carried out to form HO\({}_{2}\bullet\) through the reaction (20), but by the hydrogen extraction reaction by O\({}_{2}\) in the reaction (21), which was consistent with the experimental conclusion of [PERSON] et al. [80]. \[\mathrm{CH_{3}COOH+HO\bullet}\xrightarrow{}\mathrm{CH_{3}COO\bullet+H_{2}O} \tag{20}\]  
\[\mathrm{CH_{3}COOH+O_{2}}\xrightarrow{}\mathrm{CH_{3}COO\bullet+HO_{2}\bullet} \tag{21}\]  
Although some scholars have found through macroscopic experiments that the difference between the effect of H\({}_{2}\)O\({}_{2}\) and O\({}_{2}\) is due to different effective products at different heating temperatures, the conversion mechanisms of H\({}_{2}\)O\({}_{2}\) and O\({}_{2}\) in SCW are still limited. Additionally, since the different effects of O\({}_{2}\) and H\({}_{2}\)O\({}_{2}\) may also be related to the structural properties of organic compounds, the microscopic reaction mechanisms of organics with different structural characteristics with O\({}_{2}\) and H\({}_{2}\)O\({}_{2}\) remain to be explored.
# Numerical Investigation of the Pile-Soil Interaction Problem under Dynamic Loads
## 4 Parametric Studies
### Effect of Clay Liner Thickness  
In the experimental study, the length of the piles in the clay was 9 m, and the pile length in the sand was 4.8 m. The effect of changing the clay soil layer thickness on the bending moment and pile cap displacement was investigated. For this purpose, the situation in which the pile was completely buried in clay and the clay layer thicknesses of 3, 6, 9, and 12 m were analyzed. In these analyses, groundwater was not considered in the numerical analyses as in the reference experiment. The pile cap displacement change obtained from the analysis is presented in Figure 10, and the maximum moment change curve is presented in Figure 11. Considering the pile cap displacement values, although there was no significant increase in the displacement values initially with the increase in the clay layer thickness, significant increases were observed in the pile cap displacement at the depth where the clay layer thickness reached approximately 75% of the total pile length. No significant increase in displacements was observed as the clay layer thickness reached larger values. This is due to the fact that the pile behaves more flexibly under kinematic conditions, as the number of sockets in the dense sand decreases. When evaluated in terms of bending moments, it is seen that the maximum moment occurs when the clay layer thickness is equal to 75% of the total pile length. It is understood that kinematic conditions increase as the socket length in dense sand reaches a certain level. The reason for the decrease in pile displacement and maximum moment in the case of the socket length being less than this value is that the pile changes to a single-layer behavior and the relative displacements along the pile are minimized.  
Figure 10: Pile cap displacement–clay layer thickness curves.
## 4 Discussion
They reported that 14 out of 19 AR-assisted pedicle screws had grade 2 or grade 3 breaches. The overall accuracy was 26.3% [5]. The second was a saw bone study using the Caduceus AR system (Caduceus, Taiwan Main Orthopedics Biotechnology Co., Taiwan). The author placed 60 screws on five saw bones with the final accuracy of 98.33% [18]. [PERSON] et al. reported another AR-assisted saw bone study. They documented a 94% accuracy rate from 80 pedicle screws [7]. According to the published series, the accuracy rate varied between different AR systems. In the current cadaveric study, the authors reported an 87.5% accuracy from 24 pedicle screw placements. Further analysis of those inaccurate screws revealed that there were eight screws with grade 3 breaches in the current study. Among them, five screws had grade 3 lateral breaches over bilateral L1/2 and Rt L3. The torso had a very narrow pedicle over L1 and L2 (<5 mm), and both surgeons utilized the in-out-in technique to place the screws. Due to poor bone quality, there was a bone fracture over the transverse process-facet junction during Rt L3 screw placement that resulted in the lateral displacement of the screw. In the post-operative CT images, the authors could appreciate the original, accurate tract before lateral displacement. All lateral breach screws were recorded, but those screws were considered accurate. There were three screws with grade 3 medial breach, which occurred over Rt T6/7/8. The three inaccurate screws were the most distal screws from the iliac reference, which might yield inaccuracy in both the navigation and the 3D hologram. In the current study, the accuracy limitation of this AR system might be within five to seven spine segments away from the reference. The more distant from the reference, the more inaccurate the system. These results provided important information for the future study of this AR system. There are limitations to the current study. First, this is a single cadaveric study. The authors are fully aware that this number is not enough. However, as a pilot study, the main purpose of this study is to validate the feasibility and accuracy of AR-assisted percutaneous screw instrumentation. More studies are required before this novel technology can be applied clinically. Meanwhile, there is room for improvement in the AR goggles. The goggles were heavy and became a burden to the surgeons. Surgeons experienced headaches and dizziness after long-time use of the AR system, which might have a negative impact on the study result. Another limitation is that the torso had poor bone quality. Part of the inaccuracy may come from the displacement of the screws or micromovement of the reference. The current study provides preliminary data, and further studies are needed to validate the feasibility of AR systems in spine surgery.