esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### SAM vs. SAM 2
#### 4.1.5 Execution Time  
Lastly, we compare the execution time of the models. Figure 10a shows the execution time comparison between SAM and SAM 2. The execution time is lower for SAM 2, as the authors of the model claim. However, the most significant improvement occurs during  
Figure 9: Best and worst delta examples (Jaccard index delta and Dice score delta). (**a**) Coronacases (SAM). (**b**) Coronacases improvement (SAM 2) (67.12% and 60.44%). (**c**) Coronacases (SAM). (**d**) Coronacases decline (SAM 2) (\(-32.58\%\) and \(-26.08\%\)). (**e**) Radiopaedia (SAM). (**f**) Radiopaedia improvement (SAM 2) (62.72% and 54.81%). (**g**) Radiopaedia (SAM). (**h**) Radiopaedia decline (SAM 2) (\(-08.80\%\) and \(-04.81\%\). (**i**) Montgomery (SAM). (**j**) Montgomery improvement (SAM 2) (13.89% and 08.47%). (**k**) Montgomery (SAM). (**l**) Montgomery decline (SAM 2) (\(-13.99\%\) and \(-08.77\%\)).  
the embedding generation phase, particularly in the image-loading process. Consequently, SAM 2 achieves a lower segmentation time than SAM. When processing a single image only once, SAM 2 is faster than SAM. However, when performing multiple segmentation operations over the same image, SAM 2 would take more time than SAM in each iteration. These results make sense, as SAM 2 prediction architecture is more complex than SAM's, primarily due to its incorporation of a memory bank to enhance contextual understanding across segmentations. This additional structure improves segmentation consistency and adaptability across related images but also introduces additional computational overhead, particularly in data transfer and memory access times. Consequently, this increased architectural complexity likely contributes to the higher input/output latency and reduced overall throughput. Even when SAM 2 is operating in 2D segmentation mode, tensor propagation through its architecture remains necessary, further impacting processing efficiency. This suggests a trade-off between improved segmentation performance and real-time execution speed.  
While evaluating SAM 2 performance, we found that SAM and SAM 2 behave differently depending on how they are executed. Figure 9(b) shows these differences.  
The first two columns represent the time it takes to segment the Montgomery dataset when model is loaded only once. In contrast, the second two columns correspond to the scenario where the model is reloaded before processing each image, repeatedly segmenting the same image instead of the entire dataset. These results show that, in order to get the most out of SAM 2, the model should be loaded only once and then used to process all images. Otherwise, SAM and SAM 2 will take the same time to segment the images. Keeping this limitation in mind could speed the segmentation process up considerably when trying different prompts over the same image.
## 4 Results and Discussion
### Analysis of the Application of Augmented Reality (AR) Technology as Part of the HHP Method  
The experience of using AR technology as part of the design process was also analyzed during the HHP evaluation experiment. The majority of participants perceived that the technology is useful, being qualified as positive and realistic. However, they considered that the synchronization system  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Silky** & **Dirty** & **Abrasive** & **Rough** & **Clean** & **Slippery** & **Adherent** & \(\Sigma\) & **\%** \\ \hline
**C** & 55 & 53 & 64 & 47 & 58 & 36 & 26 & 339 & 43.2\% \\
**C\(\pm\)1** & 37 & 37 & 33 & 49 & 30 & 53 & 57 & 296 & 37.8\% \\
**C\(\pm\)2** & 16 & 13 & 13 & 9 & 18 & 18 & 20 & 107 & 13.6\% \\
**C\(\pm\)3** & 4 & 7 & 1 & 7 & 5 & 5 & 8 & 37 & 4.7\% \\
**C\(\pm\)4** & 0 & 2 & 1 & 0 & 1 & 0 & 1 & 5 & 0.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of cumulative responses according to the degree of coincidence for products A and B.  
Figure 13: Percentage of responses and match levels for products A and B and their trendline.  
of the virtual image and the texture sample could be improved with the elimination of the QR code. The study of the application of the AR without code is considered of interest to improve the interaction of the prototype with the user. To this end, it has been observed the existence of AR software tools in which a photograph is used as a reference image for the virtual object [52]. This can be applied using the physical sample, although it is necessary to study the dynamic relationship between the user and the hybrid prototype since the virtual image could be lost with the movement of the physical pattern.  
In addition, it should be noted that the application of AR has been validated as a technique used in the perception of a product, so it can be improved through the application of rendering motors with photorealistic materials.  
On the other hand, in previous investigations prototypes and methodologies can be found that measure the functionality of a product and that use the new technologies of VR, AR, and MR to reproduce experiences close to those lived by the users in the real circumstances of their use. This is the case of the Smart Hybrid Prototyping (SHP), which was created for the rapid and efficient evaluation of mechatronic products early in the product design phase [53]. All these experiences use VR to generate an environment and, for this, facilities and technical preparation that require a relatively high investment are necessary [54]. In this context, the study carried out shows that HHPs improve these technical aspects through the use of AR, allowing an integration of the element created in a real environment, and not the other way around. This facilitates user immersion, creating more realistic experiences. In this way, the realization of these hybrid prototypes is less expensive, besides being faster to implement, with simple software preparation.
## 2 Materials and Methods
### Data Collection
#### 2.3.2 GPS Data Collection  
Each player wore a Catapult Evo GPS unit ([[https://www.catapult.com](https://www.catapult.com)]([https://www.catapult.com](https://www.catapult.com)), accessed on 15 April 2021) during training and match-play. The GPS unit is only a few millimeters in size and encased in a special pouch within the playing jersey on the player's back, between their shoulder blades. The position of the GPS unit on the athlete's thoracic spine did not hinder their upper limb or torso range of motion during training or match-play [4].  
Each player's device was switched on, and a satellite lock established before training or matches started. Players wore the same allocated device for training and matches, to keep inter-unit error to a minimum [31; 32]. After each training session and match, the GPS units were collected and the data downloaded using OpenField proprietary software (Catapult, OpenField Version 14.1). The data were synced to Catapult's cloud platform, exported to a Microsoft Excel spreadsheet, and downloaded to a local computer for further analysis.  
Figure 1: Flow chart of total sampling population explaining the inclusion and exclusion criteria.  
Catapult is validated for various contact and non-contact sports settings [7; 19; 32; 33; 34]. The device uses tri-axial accelerometers (up/down, forwards/backwards, and sideways) that measure acceleration at 10,000 Hz and record at 100 Hz (100 times per second). The tri-axial gyroscopes, used along with the accelerometers, enable the device to measure acceleration, force, rotation, and body orientation with detailed movement assessment at the time of impact. It has a microprocessor that records over a thousand data points per second in real-time, ample storage on the device for post-session analysis, a powerful battery, and a GPS tracking engine. The frequency (collisions per minute) indicates the short, high-intensity bursts experienced over an entire match of 80 min. Collision per minute indicates how often a player position experience contact during their time on the field. The higher the frequency, the higher the volume of contact exposure is for a specific match [6; 7; 19; 34; 35; 36].
# The MADAG Strategy for Fault Location Techniques
## 5 Discussion
### Test Case Prioritization and Selection  
Test case prioritization is a regression testing technique concerned with the ordering of test cases for early maximization of some properties, such as the rate of fault detection [32; 35] whereas test selection aims at selecting some test cases from a given test suite for a special purpose. The test suite minimization aims at identifying redundant test cases and removing them to reduce the cost of test cases from the test suite. Test case prioritization and selection approaches often rely on structural coverage, such as statements and branches [20; 36; 37]. [PERSON] et al. [20] summarized some criteria of test case prioritization methods based on coverage-based and fault-exposing based that can improve the rate of fault detection for the test suite. [PERSON] et al. [36] showed a family of empirical studies to investigate the effectiveness of coarse granularity techniques and fine granularity techniques. [PERSON] et al. [37] suggested that the additional greedy algorithm should be used for regression test case prioritization. The other test suite reduction approach such as Dynamic Basic Block (DBB) [38] focuses on the number of DBBs and [PERSON] et al. [24] further applied ambiguity groups for test suite reduction. Although [PERSON] et al. both consider the testing effort (\(C_{t}\)) and inspection cost (\(C_{d}\)), the goal of this approach is to minimize overall cost (\(C_{t}+C_{d}\)), they do not separately consider the test case when a fault is actually detected during the execution of the prioritized test suite. However, this article considers the reduction ratio of the test case and subsequently applies selected test cases by the MADAG strategy to SBFL to discriminate the effectiveness of fault localization compared with all test cases.
# Advances in Chronic Kidney Disease in Africa
## 6 Pregnancy Related AKI and Its Causes--A Growing Concern of CKD in Africa  
Pregnancy-related acute kidney injury (PRAKI) is defined as AKI occurring during pregnancy, labour, delivery, and/or the post-partum period. The reported incidence ofPRAKI in developing nations is higher than developed countries (4-26% vs. 1-2.8%, respectively) [50; 51; 52]. PRAKI in Africa is estimated to occur in 1 in 1000 deliveries [53]. It also accounts for a significant proportion of AKI in adults in Africa (5-27%) [53]. This is 20-100-fold higher than in developed countries. [54]. In low-low-middle income countries, pre-eclampsia [PET], post-partum haemorrhage, and sepsis are the leading causes of PRAKI [50; 51; 52]. Severe or repeated episodes of AKI are risk factors for CKD, and PRAKI is increasingly recognized as a risk for developing future CKD in women. This progression to CKD adds an additional load onto the substantial health care burden in African countries.  
Kidney recovery after PRAKI is poorly described in literature from Africa. A systematic review and meta-analysis on PRAKI in SSA found only 9 of the 14 studies reported on kidney recovery data [53]. Non recovery ranged from 0.4-36.5%. Table 1 demonstrates the data from Africa with kidney recovery data following PRAKI in different cohorts [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68]. Full recovery ranged from 39-84%, and dialysis dependence ranged from 0.4-35% of those not recovering. Literature from other developing countries reported that 1.5-2.5% of patients with PRAKI progressed to ESKD, and 4-9% of women remain dialysis-dependent at 6 months post-partum in severe PRAKI [69; 70; 71; 72; 73]. Studies have demonstrated that the risk for CKD progressively increases with the severity of AKI. The adjusted HR was 2.0 for mild AKI and increased to 28.2 for severe AKI [74]. An additional concern is the high rate of loss to follow up in post-partum women. This raises major concerns for (1) gaps in the health care system, (2) lack of awareness of the need for follow-up after AKI, and (3) the known rates of incomplete recovery post PET are likely being vastly underestimated. South Africa reported a high risk of PRAKI (17%) in women with preeclampsia. Of those women that survived, 1/3 had not recovered kidney function at discharge, and half did not have a follow-up kidney function performed [65].
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 2 Methods
### Initial Alignment Based on RGB Images
#### 2.1.3 Elimination of Mismatched Pairs with RANSAC  
In order to further improve the matching accuracy of the feature points, the mismatched point pairs in the matching need to be removed. In our work, the RANSAC algorithm was used to reject incorrect matching point pairs [22] and improved the matching accuracy. RANSAC uses a random sample of data as a basis for calculating the rest of the data, and by iterating over the data and reaching a threshold number of samples, a parametric model that satisfies the largest number of data points is obtained. The iteratively calculated dataset contains both correct data and abnormal data, with the correct data usually viewed as inliers and the abnormal data denoted as outliers. Noise in the data and exceptional values in the fitted model are usually regarded as outliers, and in this research outliers are represented as pairs of incorrectly matched points. The RANSAC in our research method was performed using the following steps:  
1. [label=(0)]
2. Obtain sample set Q through feature point matching experiments in Section 2.1.2, and randomly select 4 pairs of corresponding points from set Q to form set S.
3. Use set S to calculate the homography matrix \(H_{R}\), denoted as model M. The method for solving the homography matrix is presented in Section 2.1.4.  
Figure 3: Construction of k-d trees.  
Calculate the projection error between all data points in set Q and model M. If the error is less than the preset threshold \(T_{h}\), add the corresponding data point pair to the inner point set I. Here, \(X_{1}\) and \(X_{2}\) denote the matching points, the projection error can be expressed as \[T_{h}<||X_{2}-H_{R}X_{1}||\] (1) Re-randomly sample to obtain a new set S. Then, repeat steps (2) and (3). If the number of elements in the current set of interior point I is greater than the optimal set of interior points I\({}_{\text{best}}\) (where I\({}_{\text{best}}\) represents the set with the most elements), update I\({}_{\text{best}}=1\) and the number of random samples simultaneously until the number of samples reaches k (default 200).
5. After completing k samples, I\({}_{\text{best}}\) is selected as the dataset of the optimized feature point pairs.
## 4 Cases Studies
### Performance Analysis
#### 4.2.2 Comparison with Conventional EWT  
First, by using the same treatment method, the adjacent singular value difference graph of EWT-SVD is presented in Figure 13. Because there is no clear zero value point, the number of original signals cannot be clearly estimated from the figure.  
For better comparison with the method proposed in this paper, the number of separated modes was also set to four. EWT was used to decompose the original signal in the same way. As a result, no obvious cyclical impact is apparent in the angle domain spectrum (Figure 14), and no fault features can be found in the corresponding envelope order spectra (Figure 15) of the modes extracted by EWT.  
Figure 14: Angle domain waveform of extracted modes for the acoustic signal by empirical wavelet transform (EWT).  
Figure 13: A line graph of adjacent singular value differences obtained by the EWT-SVD method.  
These results demonstrate the problems that EWT presents in dealing with the signals mentioned herein. Due to the existence of high-frequency modulation phenomena, the EWT method cannot detect the correct boundary, and thus cannot extract the fault features. Therefore, it is evident from Figure 15 that the components still contain redundant information after EWT decomposition.
## 4 Centralized Scheduling Algorithm for the IEEE802.15.4e TSCH Network
### Network Models  
In this research, the scheduling algorithm is implemented in a tree topology network represented by a directed graph \(G=(V,E)\), where \(V=\{n_{0},n_{1},...,n_{N-1}\}\) is a set of devices, and \(|V|=N\) is the total number of nodes in the network. \(n_{0}\) is the master node (PAN coordinator), and \(1\leq i\leq(N-1)\) is the \(i\)-th generic node on the network (FFD or RFD). Figure 7 shows graph \(G\) for a network. The PAN coordinator knows graph \(G\) and the traffic load generated by each node, that is, \(\widetilde{a_{i}},\ \forall\ n_{i}\in V\), and \(i\
eq 0\). Each node has (i) a parent node, \(p_{i}\); (ii) a collection of child nodes, \(ch\) (\(n_{i}\)); and (iii) a sub-tree \(ST\) (\(n_{i}\)), which is composed of \(n_{i}\) itself and all nodes connected to it via multihop paths.  
In Figure 7, it is clear that \(ST\)(\(n_{0}\)) represents the entire network, \(G\). Each node is connected to its parent \(p_{i}\) through a dedicated link, (\(n_{i},p_{i}\)) \(\in E\subset(V\times V)\). Thus, there are a number of links \(|E|=N-1\). In this study, it is assumed that all links are dedicated to avoiding collisions and reliable links are available. Each node \(n_{i}\) is only connected to its parent \(p_{i}\) even though the node has many neighbors.
## 5 Results
### Energy Consumption  
In this subsection, the energy consumption of the drone is presented.  
The energy consumption of a quadcopter drone is presented in Equation (8), where the minimum flight power for hovering at the lowest height (\(\beta\)) is 30, the multiplier for the velocity of the motor (\(\alpha\)) is 10.5, and the maximum power that the motor can achieve (\(P_{max}\)) is 85, according to [51]. The flying time (\(t\)) will be set to 20 min as it is the typical nominal endurance for quadcopter drones.  
\[Energy=(\beta+\alpha*fh)t+\ P_{max}(fh/fv) \tag{8}\]  
For quadcopter drones, the energy consumption of the drone is highly dependent on the flying height. To increase the coverage time, some authors have presented proposals for flight plans where the drone circles the node on the ground until all the data are transmitted. In our case, we consider the transmission of small amounts of data gathered from soil monitoring sensors. Therefore, that type of flight plan is not necessary for our scenario.  
The energy consumption of the quadcopter drone for a flying time of 20 min is presented in Figure 14. The energy consumption is height dependent, and the speed of the drone barely creates a difference in energy consumption. Considering that flying heights between 15 and 25 m provide better coverage, the selection of a flying height within that range would minimize the energy consumption while maximizing the coverage of the node placed at the drone.  
Considering the different node densities studied in this paper, the approximated number of nodes that the drone can cover in a flight of 20 min at different _fv_ values is presented in Table 4. This calculation was performed determining the space covered by the drone at each _fv_ taking into account that the nodes are placed at the center of a square sector of 60 m\({}^{2}\), 750 m\({}^{2}\) or 5000 m\({}^{2}\), according to the node density, and the drone flies over them in a straight line to gather the information. Therefore, for a node density of one node per 60 m\({}^{2}\) the number of nodes that can be covered is considerably greater than that of the other two configurations.  
Figure 14: Energy consumption of a quadcopter drone at 10 m/s for different _fh_.
## 4 Simulation Validation
### Simulation Setup  
The dimensions of the aluminum plate employed for the simulation were 200 mm \(\times\) 100 mm \(\times\) 1 mm. In order to have better nonlinear effect, a 3 mm-long crack was set at the center of the plate with an initial clearance of zero between the two interfaces, as shown in Figure 9. Additionally, in order to realize very minimal or no wave reflection from the boundary, a gradually damping artificial boundary was introduced to simulate a non-reflecting boundary condition [33]. The boundary was divided into 25 layers, and the damping coefficients of the individual layers were gradually increased from the inner layer to the finite boundary. Both the boundary length and the damping coefficient of each layer are dependent on the excitation frequency. The intact state without crack and the current state with the crack were studied, separately.  
The Young's modulus, Poisson's ratio and density of the aluminum plate were 69 GPa, 0.33 and 2700 kg/m\({}^{3}\), respectively. The non-reflecting boundary had the same material properties with the aluminum plate except having the damping coefficients. Moreover, the crack was modeled by embedding a seam crack at the center of the plate. Hence, it was initially closed, but it could be opened when Lamb waves traveled through. Hard normal contact and frictionless tangential contact were [22] applied to the interfaces of the seam crack to prevent nodes penetration. Thus, a breathing behavior appeared when Lamb waves interacted with the crack.  
Figure 9: Schematic diagram of the aluminum plate with a breathing crack in FE simulation.  
Hanning-window modulated five-cycle sinusoidal tone bursts at a central frequency of 250 kHz were excited by applying a pair of symmetric displacement on two points, which were symmetrically positioned on the upper and lower surfaces of the plate. Previous work [34] has described in detail this type of point displacement modeling to generate single S0 mode Lamb wave. The displacement U3 at one point on the upper surface was monitored as the received signal. A global cartesian coordinate was also introduced, and the bottom-left corner of the plate was at the origin. The points for exciting and receiving signals were labeled as A, B, C, and D at coordinates (25, 10), (25, 90), (175, 90), (175, 10), respectively.  
In order to obtain sufficient accuracy and high efficiency, the dimension of the elements was around 0.5 mm \(\times\) 0.5 mm \(\times\) 0.25 mm, such that there were at least 20 elements per wavelength of the Lamb waves. This spatial resolution was chosen to warrant simulation precision and error convergence. Smaller elements were applied in the vicinity of the crack because of complicated mechanical response at the crack zone, and the elements were meshed automatically. Eight-noded 3D reduced integrated linear brick elements, C3D8R, were used to model the aluminum plate and the boundary. A time step of 2 ns was used to ensure the accuracy of the second harmonic generation [35].