esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 1 Introduction  
Buried pipe systems play an important role in modern life, providing essential utility services especially in the transportation of resources for daily use. The latest data from 2014 [1] gives a total of slightly less than 2,175,000 miles of pipeline in 120 countries of the world [2]. The number of buried pipelines is growing rapidly in response to the demand from communities, which makes the task of locating these buried pipes highly difficult. Correspondingly, various detection techniques have been developed in order to rectify this problem.  
Traditionally, metal detectors [3, 4] and ground penetrating radar (GPR) [5, 6, 7] have been the primary means of locating buried pipelines. However, plastic pipes have been increasingly usedin recent decades because of their low cost and low failure rates [8], which makes metal detectors inapplicable in most scenarios. GPR is serviceable for both metallic and non-metallic objects detection; nevertheless, the high attenuation of electromagnetic wave in conductive materials renders it inefficient in the presence of wet soil. An alternative option is to employ acoustic locating techniques, which rely on the mismatch of buried objects and surrounding medium in terms of acoustic properties, as well as the propagation of mechanical waves instead of electromagnetic waves.  
The acoustic transmission method, as a trenchless technique, has been applied in underground utility detection and proven as a useful and effective approach for locating both metallic and non-metallic buried utilities [9; 10; 11]. Recently vibro-acoustic methods [12] have been developed under the frame of 'Mapping the Underworld' [13; 14], which has shown very promising in locating buried pipelines, especially for plastic pipes. Compared with electromagnetic-based techniques, such as metal locators and GPR, acoustic methods possess the advantage of efficiency for a wide range of soil types and non-metallic object detection.  
This paper reviews the acoustic methods pertaining to the localization of buried pipes. The investigation starts with the literature review on the theoretical study of wave propagation mechanism in buried pipe systems in Section 2, followed by a detailed description of the state-of-the-art acoustic techniques for locating buried pipes in Section 3. The principles behind the techniques, as well as the applicability and limitation of each method are discussed. Section 4 presents several representative acoustic locators that are commercially available to provide references for practical use. Factors which may influence on the measurement results are discussed in Section 5. Following this, decision analysis of preferred pipeline localization methods is presented in Section 6. Finally, the conclusions and recommendations for future research are given in Section 7.
# Comparison of CAD/CAM and Conventional Denture Base Resins: A Systematic Review
## 3 Results
### Physical Properties  
Seven studies compared the surface roughness of CAD/CAM and conventional resins [22; 24; 28; 29; 30; 31; 32]. Surface roughness was measured using laser or contact profilometers. Three of the seven studies showed that conventional resins had significantly higher surface roughness than CAD/CAM resins [22; 28; 32]. Another study also showed that conventional resins had a higher roughness, except for one brand of CAD/CAM resin that showed a slightly lower roughness that was not statistically significant [30]. The study by [PERSON] et al. showed that two of the five CAD/CAM resins tested had lower roughness than conventional resins [29]. One study showed that surface roughness was significantly higher for CAD/CAM resins [24]. The study by [PERSON] et al. showed that the resins did not exhibit different roughness values before and after thermal cycling [31]. The data are summarized in Table 4.  
Four studies have focused on the hydrophobicity of resins using the sessile drop technique with calculation of the contact angle [22; 30; 31; 32]. The results of the studies are presented in Table 5. Two studies showed that most CAD/CAM resins were more hydrophobic than conventional resins [22; 31], while another study showed a higher hydrophobicity rate for conventional resins [30]. All the studies highlighted variations in hydrophobicity according to the CAD/CAM resin used [22; 30; 31; 32].  
One study used thermocycling to simulate the clinical aging process and found that conventional resins become more hydrophobic and CAD/CAM resins become more hydrophilic after aging [31].
## 4 Experiments and Results
### Combining Manual and Auto-Labels for Object Detection Experiment  
Finally, we trained and evaluated object detection using labels that combined both manual and auto-labels. The results are presented in Table 8, and it was observed that the overall performance was highest when training with combined manual labels, auto-labels, and both. This suggests that assigning weights to important objects contributed to the superior performance in the combined label scenario.  
When training group 2 with auto-labels, the overall evaluation performance was lower compared to manual labels. It is interpreted that the auto-labels generated by Grounding DINO, trained on a large-scale dataset, may not be well-suited for angles looking from top to bottom, such as in CCTV footage. It is assumed that the quality of auto-labels is relatively lower for CCTV videos. Detection test results trained with manual labels show a significant performance difference among device groups, whereas test results using auto-labels reduce the gap between groups. This indicates that auto-labels provide consistent labeling without significant bias compared to manual labels.  
Confirmation of no performance degradation when using auto-labels for object detection, and the ability to enhance performance by combining auto-labels with manual labels, was observed. To improve accuracy and efficiency, a comparison between auto-labels and manual labels was conducted, along with an analysis of the impact of different device types on each other.
# Validation of Acoustic Emission Waveform Entropy as a Damage Identification Feature
## 5 Validation and Discussion
### Correlation Between the Coupon Specim and Real Structures under Operation
By considering this relation, it is suggested in reference [22] that in austenitic stainless steel, with the increase in average velocity of the dislocation (due to higher strain rate), the \(V_{rms}\) of the waveform also increases. \[{V_{rms}}^{2}=p_{m}bv_{av} \tag{5}\]  
Therefore, it can be suggested that the higher strain rate of the fatigue test during yielding resulted in the generation of waveform with larger \(V_{rms}\), which in turn resulted in the noticeable increase in AE entropy. In the cargo tank, the maximum duration of sloshing impact is 200 ms, which is identical to the duration of respective load in fatigue test. Owing to this reason, a strong sloshing impact has the potential to cause deformation in the primary wall with a significantly higher strain rate (comparable to the one in the fatigue test) than the standard tensile test of this research. Therefore, the deformation in the primary wall due to sloshing can be associated with a noticeable increase in AE features. AE entropy in particular can be expected to be above 10 during the deformation caused by sloshing.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 3 Dangampo Site: Equipment Testing
### Results and Discussion
#### 3.2.5 Second Excavation: Result Verification and Method Refinement  
Accordingly, the SS was selected for use during the second UE conducted in 2018 to record the SRA at the Dangampo site. During the second excavation with an SRA of 20 m \(\times\) 20 m, the remaining sediment from the first excavation was surveyed. A 10 m \(\times\) 10 m square area to the east of the sediment area removed in the first excavation was selected and subdivided into a grid of four 5 m \(\times\) 5 m squares to continue the sediment removal operation (Figure 5).  
To record the SRA, the SS transducer was positioned next to it and operated at a frequency of 675 kHz, with a swath varying between 25 and 40 m. The 25 m swath was not sufficient to capture the entire SRA, which was larger during the second excavation than the first removal operation. Using a 40 m swath, the entire SRA was captured. However, the resolution decreased with increasing distance from the center. To address this problem, SS images were collected at three points around the SRA using the 25 m swath. The three images were combined into one mosaic image of the entire area. The mosaic image of the three 25 m swath images captured at three different positions had a higher resolution than the image taken by the 40 m swath at one point (Figure 6).  
Figure 5: Sediment removal area of the second underwater excavation at the Dangampo site.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**PA as a Moderator of Relationships between**} & \multicolumn{3}{c}{**PA as a Moderator of Relationships between**} \\  & & \multicolumn{3}{c}{**BMI and Social Relationshipships**} & \multicolumn{3}{c}{**BMI and Environmental Health**} \\  & & **Beta** & **Sig.** & \(\mathbf{R^{2}}\) & **Sig.** & **Beta** & **Sig.** & \(\mathbf{R^{2}}\) & **Sig.** \\ \hline
**Model 1** & BMI & \(-0.084\) & 0.251 & 0.007 & 0.251 & 0.052 & 0.477 & 0.003 & 0.477 \\  & BMI & \(-0.013\) & 0.860 & & 0.059 & 0.436 & & \\  & Walking & 0.034 & 0.651 & 0.074 & & \(-0.011\) & 0.891 & 0.025 & \\
**Model 2** & Moderate PA & 0.142 & 0.070 & (0.005) & & 0.161 & 0.045 & (0.243) & 0.321 \\  & Vigorous PA & \(-0.299\) & 0.000 & & & \(-0.038\) & 0.656 & & \\  & BMI & \(-0.269\) & 0.038 & & & \(-0.304\) & 0.024 & & \\  & Walking & \(-0.239\) & 0.569 & & & \(-1.032\) & 0.019 & & \\  & Moderate PA & 1.312 & 0.085 & 0.154 & & 0.167 & 0.833 & 0.088 & \\
**Model 3** & Vigorous PA & \(-2.866\) & 0.000 & (0.001) & & \(-1.352\) & 0.051 & (0.007) & 0.018 \\  & BMI \(\times\) Walking & 0.289 & 0.500 & & & 1.073 & **0.017** & & \\  & BMI \(\times\) Moderate PA & \(-1.152\) & 0.133 & & & 0.020 & 0.980 & & \\  & BMI \(\times\) Vigorous PA & 2.691 & **0.000** & & & 1.410 & **0.049** & & \\ \hline \hline \end{tabular}  
* **Legend: PA—**physical activity, **BMI—**body mass index, **Beta—**standard partial regression coefficient of each predictor variable on the criterion, **Sig.—**—**level of significance, \(\mathbf{R^{2}}\)—coefficient of multiple determination of the variable and predictor system.  
\end{table} 
Table 3: Physical activity as a moderator of the relationships among BMI, social relationships, and environmental health. for social relationships (Sig. = 0.041) was determined, which meant that PA affected the relationship between BI and these QoL domains. The results showed that both walking (Sig. = 0.035) and moderate PA (Sig. = 0.032) moderated the relationship between BI and social relationships in adults, while walking (Sig. = 0.000) and vigorous PA (Sig. = 0.016) moderated the relationship between BI and environmental health in the sample of the working population.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 2 Applications of Personalized Video Summarization
### Personalized Indoor Video Summary  
Creating a personalized home video summary is very important, as each user can view events that interest them according to their preferences without having to watch the entire video. [PERSON] et al. [46] presented an interactive mobile-based real-time home video summarization application that reflects user preferences. [PERSON] and [PERSON] [26] presented a method by which the video summarization of life logs from multiple sources is performed in an office environment. [PERSON] et al. [27] presented a system to automatically generate personalized home video summaries based on user behavior.
# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 3 Energy-Throughput Efficient Cross-Layer Solution Using MPNMRA for WUSNs
### Geographical Routing Algorithm  
The concept of a geographical routing protocol [10] holds promise for underground environments due to its ability to operate with limited signaling and scalability requirements. In this protocol, a source node \(i\) selects the best forwarder \(j^{*}\) from its set of neighbors (\(S_{i}\)) based on a specific objective function, i.e., \(S_{i}\cap F_{i}^{D}\), where \(F_{i}^{D}\) is the forwarding set of node \(i\). The objective function can be designed to optimize various parameters such as energy efficiency, shortest path, or minimum delay.  
Geographical routing protocols rely on the knowledge of node locations and network topology. Each node is typically equipped with location information, either obtained through GPS or other localization techniques. With this information, a source node \(i\) can determine its neighbors and select the best forwarder \(j^{*}\) among them. The selection of the best forwarder is based on the objective function defined for the routing protocol. This function considers factors such as link quality, remaining energy of nodes, or proximity to the destination. By evaluating these parameters, the source node \(i\) can identify the optimal forwarder \(j^{*}\) to relay its packets towards the destination. The use of geographical routing protocols in underground environments helps in achieving efficient and reliable communication with limited signaling overhead and improved scalability. By leveraging location information and selecting the best forwarder based on the defined objective function, these protocols enable effective routing in WUSNs while considering the unique characteristics and challenges of underground environments.
## 3 Results
### Summary of Signal Amplification  
Table 1 illustrates a discrete analysis of the active area and amplifier board percentage gains that were observed in Sections 3.1-3.5. From the table, the active area amplification mode yielded an average signal augmentation of 91.16% across the four X-ray parameter variations. On the other hand, the amplifier board amplification mode yielded a signal augmentation of 36.48% for the tested X-ray parameters in this study.
## OpenACC Implementation
### OpenACC Programming
#### 2.3.4. Optimization by Tuning Number of Vectors  
The OpenACC performance model includes three sections: Gang (Thread block), Worker (Warp), and Vector (Threads in a Warp) [38].  
Gangs, vectors, and workers can be automatically set up by the OpenACC compiler or by the developer to yield the optimum approach. The number of vectors is modified in the OpenACC directives to gain the maximum occupancy and speed-up in GPU. Modifying the vector length clause can be beneficial to reaching the optimal runtime value by trying different vector lengths for the accelerator, which was used in this study. Figure 6 shows the relative runtime derived by varying the vector length compared to the compiler-selected value. It is worth noting that the best performance was obtained for a vector length of 1024.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Programming Language** & **Directive** & **Runtime(s)** & **Speed-Up** \\ \hline C (without optimization) & - & 2231.59 & 1 X \\ \hline C (with optimization) & - & 914.98 & 2.43 X \\ \hline \multirow{2}{*}{OpenACC} & \#pragma ACC parallel loop & 212.55 & 10.49 X \\  & \#pragma ACC kernels loop & 200.21 & \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of OpenACC directive.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Programming Language** & **Variable Precision** & **Runtime(s)** & **Speed-Up** \\ \hline \multirow{2}{*}{C (without optimization)} & double & 2231.59 & 1 X \\  & float & 2269.56 & 0.98 X \\ \hline \multirow{2}{*}{OpenACC} & double & 200.51 & 11.01 X \\  & float & 111.47 & 20.02 X \\ \hline \end{tabular}
\end{table}
Table 3: Increase in the computational speed regarding the accuracy of the variables stored.  
Figure 6: Relative runtime from varying vector length from the default value.
## 2 Materials and Methods
### Calculation of Tile Locations
Therefore during the process of tile calculations, it is important to specify the zoom level for a given coordinate. So, it is often necessary to convert various coordinate values to another zoom level. All the filtering steps for example in our pipeline are performed on a scaled-down version by the factor 32 of the WSI to enhance the speed and obtain the results in a reasonable time. A Rectangle object represents the bounds of a tile. It also wraps necessary functionality, such as rotation. The Grid class implements all the functionality to represent a grid of Rectangles and, therefore, possible tile locations that are laid over a ROI. Finally, there is the GridManager class. It creates as many Grid objects for each ROI as is specified in \"grids_per_roi\" and contains some convenience functions for, e.g., visualization. It also merges overlapping ROIs. The full spectrum of the functionality of these classes can be seen on GitHub: [[https://github.com/FAU-DLM/wsi_processing_pipeline/blob/master](https://github.com/FAU-DLM/wsi_processing_pipeline/blob/master)]([https://github.com/FAU-DLM/wsi_processing_pipeline/blob/master](https://github.com/FAU-DLM/wsi_processing_pipeline/blob/master)) /tile_extraction/tiles.py#L78, accessed on 15 December 2021. If \"grids_per_roi\" is greater than one, multiple slightly shifted grids are laid over each ROI. This increases the number of tiles and therefore the amount of training data. This means that the same tissue is present in multiple tiles but, nonetheless, all tiles are unique. If \"optimize_grid_angles\" is true, the grid is rotated in an iterative approach by \"angle_stepsize\" in each iteration, and the angle, which results in the most tiles per ROI, will be used for further calculations. This is completed for each ROI individually. So the smaller the \"angle_stepsize\" is, the closer the angle gets to the optimum, but the longer the process takes. The last important parameter in this context is \"minimal_tile_roi_intersection_ratio\". If it is 1.0, only tiles that lay 100% in the ROI will be considered for further processing. The closer it gets to 0.0, the more tiles can be outside of the ROI, but never completely, since 0.0 is outside of the possible range of this value.
# Collaborative Robotics: Enhance Maintenance Procedures on Primary Flight Control Servo-Actuators
## 3 Critical Aspects Related to the Automatic Adjustments Procedures
### Sensors-Related Challenges
#### 3.2.2 Camera Calibration  
The vision system adopted is a 2D monochrome camera that can perform fast and accurate inspections on a wide range of mechanical components. Once a picture has been acquired, it is possible to extract coordinates and angles of several elements in its Field Of View (FOV). Through the camera software, these values are then used to compute the actual coordinates of the waypoints on the servo-actuator, which are then stored inside a database. However, the raw data provided by the vision system are in pixels, while the robot operates according to the metric system. The conversion is achieved by using the checkerboard sheet, reported in Figure 7b, through which the camera is calibrated. This calibration step is required for all the distances between the sensor and the object of interest on the servo-actuator. The results of all the calibration procedures are recorded inside the camera software for later use. When the Central Computer commands the camera to take a picture to locate the exact location of specific points on the faulty unit, it provides the information about which pixels-to-meters mapping to use, leading to a proper conversion.  
Moreover, to minimize the fisheye effect, it is crucial to place the L-shaped pattern in the checkerboard, called _fiducial_, and the objects on the EHSA to be captured in the center of the camera's field of view. Otherwise, the image distortion could negatively affect the pixels-to-meters conversion, leading to erroneous waypoints coordinates.
## 3 Methodology
### Logical Rules Embedded  
The logical rules in this paper are derived from the ontology model used to guide the construction of assembly process KG, which is expressed in the triplet form of _<head entity type, relation, tail entity type_>:  
\[LR=(ht,r,tt) \tag{8}\]  
The ontology model for assembly process KG construction in this paper is constructed in a top-down manner, which consist of layers of knowledge case, operation and step for assembly process specification. operation layer and step layer are associated with assembly resource. The ontology model guides the construction of KG, thus ensuring the correctness of logical rules.  
Regarding the mapping of logical rules to vector spaces, it is crucial to note that the differentiation among embedded learning models primarily lies in their approaches to characterizing the mapping relations between entities and relations, while typically overlooking entity type information. Consequently, the vectorized logical rules are integrated into the model's loss function as knowledge constraints. Specifically, during model training, if the inferred result _<ht, r, tt_> derived from _<h, r, t, ht, tt_> (all represented in vector form) is absent from the knowledge constraint set established based on logical rules, a substantial penalty term is generated. This penalty mechanism serves to regulate and optimize the direction of model training. The underlying logic can be formalized as follows:  
\[P=\left\{\begin{array}{ll}C,&if\ \left(ht,r,tt\right)\
otin D_{lr}\\ 0,&if\ \left(ht,r,tt\right)\in D_{lr}\end{array}\right. \tag{9}\]  
where \(D_{lr}\) is logical rules-based dictionary.  
In addition to guiding the direction of model training, logical rules can also be used in negative sample generation. Regardless of the negative sample generation process based on entity replacement, relation replacement, and entity type replacement, as long as the replaced quintuple (head entity type, relation, and tail entity type) does not exist in the knowledge constraint set, the constructed sample could be guaranteed to be negative, that is:  
\[\left(ht,r,tt\right)\
otin D_{lr},\ if\ \left(ht,r,tt\right)\in NS \tag{10}\]  
In the equation above, \(NS\) represents the negative sample set. Negative sample filtering based on logical rules could avoid the problem that a few positive samples in the negative sample will affect the model training and the final model performance.