esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 3 Experiments and Results
### Measurement Example of 3D Model Generation by Registration of Consecutive Scans  
Additional dynamic recordings were carried out using a triaxial gantry system. A pipeline was placed in the water, as well as the ball-bar specimen and plane normal. The underwater scene is shown in the left image of Figure 8. Tests were performed with two sensor velocities of about 0.1 m/s and 0.7 m/s. The sensor distance to the measurement objects was consistently approximately 2.0 m. The measurements were carried out in clear water with a temperature of about 14 \({}^{\circ}\)C. There was a negligible amount of stray light from the very small windows of the hangar.  
The plastic pipe of about 7 m length on the floor of the basin was scanned in continuous motion of the sensor. Consecutive scans were merged according to the description in Section 2.3. The result is shown in the right image of Figure 8. The generated point cloud is composed of 680 individual scans. The point cloud is colored according to height.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Velocity**} & **Compensation** & **Standarddev.** & **Point Number** & **Standarddev.** & **Point Number** \\  & **Method** & **Sphere-Fit** & **on Sphere** & **Cylinder-Fit** & **on Cylinder** \\ \hline
0.1 m/s & No & 0.39 mm & 6800 & 0.31 mm & 69,000 \\ \hline
0.1 m/s & Manually & 0.31 mm & 7100 & 0.32 mm & 69,000 \\ \hline
0.1 m/s & Automatically & 0.33 mm & 7100 & 0.32 mm & 69,000 \\ \hline
0.7 m/s & No & 0.52 mm & 3200 & 0.39 mm & 47,500 \\ \hline
0.7 m/s & Manually & 0.36 mm & 5200 & 0.34 mm & 49,600 \\ \hline
0.7 m/s & Automatically & 0.32 mm & 6000 & 0.34 mm & 49,600 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of standard deviations of the 3D points from the fitted sphere and cylinder shape depending on sensor velocity and compensation method.  
Figure 7: 3D reconstruction result of underwater pipe measurement at 0.7 m/s sensor velocity without motion compensation (MC) (**left**), manual MC (**middle**), and automatic MC (**right**).
## 3 Depth Safety Path Planning Method  
### Analysis of Hydrodynamic Properties of Unmanned Surface Vehicle  
According to the hydrodynamic model established in Section 2.1, the navigation simulation of the USV is carried out, and the hydrodynamic properties of the USV under nine different working conditions are analyzed. The numerical simulation results are shown in Figures 6-8. The diagrams of navigation status, including heave and pitch motion in time domain under nine working conditions, are detailed in Supplementary Materials Figures S1 and S2. As shown in Figure 7a, the heave motion of the unmanned surface vehicle presents a gentle trend with the increase of speed. As shown in Figure 7b, the maximum heave amplitude is 0.7 m when the speed is 5 kn, and the significant wave height is 1.5 m, and the maximum downward settlement amplitude of the USV is 0.3 m (refer to Figure S1) when the speed is 8 kn, and the significant wave height is 1 m. It can be seen that when the USV navigates at a lower speed at a higher significant wave height, there will be a larger heave motion. In order to  
Figure 5: Electronic navigation Chart and Grid Depth Distribution Map. (**a**) S57 Vector Electronic navigation Chart displayed by S52 Standard in the study area; (**b**) Isobath Area Map; (**c**) Depth Distribution Map obtained by spline function interpolation approach with obstacles, in which the blank area is an obstacle; (**d**) Overlay with discrete depth points, contours, islands and reefs and other obstacles. In the depth distribution map, the magenta area is an obstacle.  
ensure the safety of navigation depth, the maximum downward settlement value is selected as the input parameter to calculate the minimum safe depth of navigation for unmanned surface vehicles.  
The pitch of the ship, in waves, can also cause draft changes and induce danger of striking on the sea bed. As shown in Figure 8, the pitch angle of the USV decreases with the increase of speed and significant wave height. As shown in Figure 8, the maximum pitch angle occurs at the speed of 5 kn, and the significant wave height of 1.5 m, reaching 10.5 degrees. The minimum pitch angle occurs at 11 kn, and the significant wave height is 0.5 m, which is 3.7 degrees. The USV has a larger pitch angle at lower speed and higher significant wave height, which causes larger draft change and gives rise to potential hazards to navigation safety of the USV. In order to ensure the safety of navigation  
Figure 8: Maximum pitch Angle of nine cases.  
Figure 6: Numerical simulation results of the unmanned vehicle.  
Figure 7: Analysis of heave of the USV.  
depth, the maximum pitch angle is used as the input parameter to calculate the minimum safe depth of navigation for USV.
###### Abstract  
Background and objectives.  
This paper aimed to evaluate the changes in mean bone density values of the midpalatal suture (MPS) in 392 young patients treated with a rapid palatal expander (RPE) appliance, depending on sex and vertical and sagittal skeletal patterns. **Materials and Methods.** Evaluations were performed using a low-dose protocol for cone beam computed tomography scans at T0 (preoperative) and T1 (1 year after the beginning of the therapy). The region of interest was used to calculate bone density in Hounsfield units (HU) for the area between the maxillary incisors. **Results.** CBCT scan data of 196 females and 196 males (mean age of 11.7 years) showed homogeneous and similar density values of the MPS at T0 (550.17-563.70 HU) and T1 (541.92-553.85 HU). Class III skeletal individuals showed significantly higher BD than the Class II group at T0, but not at T1. Females showed significant and substantially higher BD than males at T0 and T1. No significant differences were found between the other groups and between the two time points in terms of the bone density values of the MPS. **Conclusions.** Females and the Class III group showed significantly higher bone density values than males and Class II, respectively. No statistically significant differences were found from T0 to T1 in any groups, suggesting that a similar rate of suture reorganization occurs after the use of an RPE, causing reorganization and bone deposition along the MPS.  
rapid palatal expander; midpalatal suture; bone density; cone beam computed tomography; facial patterns; skeletal growth pattern +
Footnote †: journal: Applied sciences
# A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism
## 3 Methods
### Anchor Box Parameter Optimization  
YOLOv5 adds the a priori information of the target box to the network prediction information. Before training the network, the information range of the target box, such as the range of box center coordinates, is obtained first, then the network is trained to learn this range of values.  
The idea of adaptive box anchors (auto-learning bounding box anchors) is used in YOLOv5, and since the target size of the rebar dataset differs significantly from the COCO dataset [25], we need to recalculate the anchor box size. The specific function \(T\) for the calculation of K-means [26] clustering is shown in Equation (1).  
\[T=\text{min}\sum_{i=1}^{n}\sum_{j=1}^{k}\left[1-\frac{box_{i}\cap cen_{j}}{ box_{i}\cup cen_{j}}\right] \tag{1}\]  
The evaluation clustering is expressed by the best possible recall (\(BPR\)), which is calculated as shown in Equation (2).  
\[BPR=\frac{1}{n}\sum_{i=1}^{n}\text{max}(\frac{box_{i}\cap cen_{1}}{box_{i}\cup cen _{1}},\frac{box_{i}\cap cen_{2}}{box_{i}\cup cen_{2}},\dots,\frac{box_{i}\cap cen _{k}}{box_{i}\cup cen_{k}}) \tag{2}\]  
where box\({}_{i}\) is the area of the \(i\)th labeled box in the target, cen\({}_{j}\) is the area of the \(j\)th clustering center, \(n\) is the number of detected targets, and \(k\) is the number of clustering centers.  
The adaptive setting of K-means should conform to the anchor size of the dataset, which makes the training model easier to converge. But the clustering results are influenced by the selection of initial points, and the data need to be clustered several times until the clustering centers do not change; finally, the width and height of the anchor are derived. Therefore, we use the K-means++ [27] algorithm to perform a re-clustering analysis on the rebar cross-section dataset, which optimizes the problem that the algorithm is sensitive to the initial points, and find that the clustering results are better when the initial points are selected with a greater distance.  
The steps of the K-means++ algorithm are as follows:  
Figure 5: Semi-automatic labeling data processing flow.  
1. Randomly select samples from the dataset as cluster centers.
2. Calculate the distance \(D(x)\) between the remaining samples in the dataset and the nearest clustering center.
3. Calculate the probability \(P\) of whether the sample is the next clustering center, as shown in Equation (3). Select the clustering center based on the probability. \[P=\frac{D(x)^{2}}{\sum\limits_{x\in X}D(x)^{2}}\] (3)
4. Repeat steps (2) and (3) to select all clustering centers.
5. Iterate using the original K-means method to obtain the final results based on the obtained clustering centers.  
To match the re-clustered anchor with the multi-scale detection head of YOLOv5, the value of K is fixed to 16. We finally determined the size of the anchors after several iterations and optimizations. The final anchor boxes are assigned according to the different scales of detection layers, as shown in Table 1:
# Wake-Up Receiver with Equal-Gain Antenna Diversity +
###### Abstract  
Small scale fading signals resulting from multipath propagation can cause signal strength variations in the range of several dB. Resulting from the fluctuating signal strengths, the wake-up packet reception rate can decrease significantly. Using antenna diversity can greatly mitigate these effects. This article presents a novel wireless sensor node with wake-up receiver that uses an equal-gain diversity method with two antennas in the wake-up path. Summation of the two diversity branch signals is done after the passive demodulation of the incoming signals. As a result, the wireless sensor node requires almost no additional active parts that would increase power consumption. Furthermore, we demonstrate experimentally the improved wake-up robustness and reliability achieved by this diversity technique in a multipath environment.  
wake-up receiver; wireless sensor node; small-scale fading; antenna diversity; wake-up packet; wireless sensor network +
Footnote †: This paper is an extended version of our paper published in [PERSON], [PERSON], and [PERSON]. “Using antenna diversity to improve wake-up range and probability.” Session 4P6 RF and Wireless Communication 2091.
## 2 Materials and Methods  
The LOINC axis model is the foundation of all LOINC codes. In particular, there are two axes that play a major role in this project, these being the type of _Scale_ and the kind of _Property_ (also known as kind of quantity) associated with a quantitative observation or test result. A standard closely related to LOINC is UCUM. This coding system deals with the unambiguous communication of physical units and their associated quantities currently used in international science, business and engineering. Each UCUM unit can be represented via seven metric base units and a numeric value. This so-called canonical form is of vital importance when establishing the connection between LOINC and UCUM by mapping one such representation to each quantitative _Property_. Although there is a loose association between LOINC's quantitative _Properties_ and UCUM [6], the LOINC manual outlines no explicit way in which the two relate in detail [12]. Despite this, about half of all LOINC codes have at least one example UUUM unit associated with them in the LOINC data set. The idea behind formalizing the connection between LOINC and UUUM was to make use of the fact that each LOINC code is always associated with a _Property_. Moreover, a subset of these _Properties_ describe physical quantities which can be represented as UUUM units. In this way, the LOINC _Property_ axis acts as the mediator between the two standards, by which an explicit association was able to be established. This knowledge was then formalized via a FHIR ConceptMap resource.
# Echocardiography in the Diagnosis and Management of Tricuspid Atresia
## 5 Echo-Doppler Studies Following Palliative/Corrective Interventions
### Echocardiographic Evaluation of Interstage Issues
#### 5.4.5 Shunt Blockage  
Blockage of the aorta-pulmonary shunts may develop during follow up, which may either be complete or partial. Patients with complete blockage, usually due to thrombus formation, typically present with severe cyanosis and respiratory distress. Inability to demonstrate the shunt by color Doppler interrogation in a patient in whom a shunt was demonstrated in prior studies suggests complete shunt blockage. In patients who develop partial blockage, the obstruction may be either at the proximal anastomosis with the subclavian (or innominate) artery or aortic arch, at the distal anastomosis at the entry into the PA, or, rarely, somewhere in between (due to kinking). While these obstructive lesions are easily demonstrable on angiography (Figure 37), these narrowings are somewhat difficult to image by 2D. However, color flow disturbance along with high Doppler flow velocities at the site of obstruction suggest obstruction. Thromolytic recanalization, transcatheter stent implantation, the surgical revision of the shunt, or bidirectional Glenn may be performed, depending upon the clinical scenario and institutional preference [38].  
Figure 37: (**A**) Selected cine frame in a postero-anterior view demonstrating narrowing (arrow) of a modified Blalock–Taussig (MBT) shunt at its junction with the right innominate artery. (**B**) Similar angiographic frame from a different patient showing narrowing of the MBT (arrow) at its entry into the right pulmonary artery (RPA). C, catheter; DAo, descending aorta; LPA, left pulmonary artery.  
Figure 36: Selected video frames from apical four chamber view demonstrating dilated left atrium (LA) and left ventricle (LV) (**A**,**B**). Note the mitral insufficiency jet (MR Jet) in (**B**).
## 4 Discussion
### Alkylphenols and Chlorophenols Removal in CW Systems
Rhizodegradation of alkylphenols, specifically NP and OP, occurs in aerobic conditions in various environments [75; 76; 77]. Some microorganisms possess the ability to assimilate alkylphenols as a sole carbon and energy source [78; 79] and [PERSON] et al. [80] isolated bacteria with ability to degrade NP and OP from _P. australis_ rhizosphere. In the case of PCP, [PERSON] and [PERSON] [81] observed that under laboratory conditions no PCP degradation occurred in wetland soil which had been sterilized but the compound was degraded in nonsterile soil, suggesting that PCP degradation was due to biological activity and that most wetland soils contain microorganisms with capacity to degrade PCP despite no known history of contamination. However, these authors consider that this soil capacity was regulated by contamination level, type of electron acceptors and donors and microbial biomass. Therefore, current results indicate a potential degradation of the target compounds by microbial communities present in plant bed substrate, with the CWs system providing chemical and biological conditions for this biological process to occur. Therefore, CWs were able to remediate water contaminated with a mixture of NP and OP, with a significant removal of the compounds from the water and from the CWs systems, showing that CWs with vertical flow can be a suitable option due to the combined action of plants and microorganisms. Nevertheless, OP removal only occurred for a fresh contamination indicating the need for the compound to be bioavailable. However, for PCP, despite the high removal observed, a small percentage of the compound was still present in CWs effluent (<10%). Moreover, PCP was also detected in CW substrate indicating that the microbial community was not able to totally degrade this compound. Therefore, optimization of CW processes is needed to fully remove this compound. Microbial communities' characterization can help to highlight the processes occurring in CWs, contributing for its improvement.
## 4 Numerical Simulation of Damaged Steel Wire
### Analysis of Crack Propagation  
Both steel wires, whether with or without corrosion pits, feature two initial cracks located at the mid-section of the wire. Figure 12 illustrates the propagation morphology of single and double cracks under fatigue loading. In Figure 12a, the single crack propagates along the centerline of the crack front. Conversely, in Figure 12b,c, the double-crack scenario demonstrates that during the initial stages of crack propagation, the two cracks propagate independently at similar rates and trajectories. It is only when the tips of the two independent cracks come into contact that a significant change occurs. Following this contact, the two cracks rapidly coalesce into a single continuous crack. Subsequently, the morphology of the crack propagation stage resembles that of a single crack until the wire ultimately fractures upon reaching the critical crack depth.  
Figure 11: Influence of shape on the fatigue life of a single-crack steel wire.  
Figure 12: Process of coplanar dual crack propagation. (**a**) Single-crack propagation path. (**b**) Double-crack propagation path without pits. (**c**) Double-crack propagation path with pits.  
The propagation curve of the coalesced crack is depicted in Figure 13, which identifies three distinct stages of crack development: independent propagation, crack coalescence, and post-coalescence propagation. Notably, the rapid expansion and coalescence of the two cracks during this process consume a substantial number of fatigue cycles. Furthermore, corrosion pits have been shown to accelerate the independent growth of cracks throughout this process. The initial sizes of the cracks, as well as the spacing between adjacent cracks, significantly influence the propagation dynamics of multiple cracks in the steel wire. When adjacent cracks are in close proximity, their interaction becomes a critical consideration for assessing fatigue performance.
## 4 Discussion  
1. The proposed method performs instance weighting using multi-objective instance weights for effective transfer learning to minimize the discrepancy between the target and source domain instances. Each source instance is assigned a weight that evaluates the relation to the target domain to curtail the negative transfer effect resulting from source instances with a large domain discrepancy. The MMD and KLD, which are indicators measuring dissimilarity, are used to obtain these weights. This method considers the discrepancy between the source and target domains and the discrepancy within the source domain. It includes the process of finding the optimal multi-objective instance weights and the final transfer learning process. The effect of knowledge transfer between the source and target domains is maximized through this method.
2. It was confirmed that the target task performance increased using a different but related source domain. However, some instances are not related to the source domain and have a large discrepancy. Therefore, performance improvement varies greatly depending on the technique of using the source domain. In this paper, we compared the performance of the domain optimization techniques used for this purpose. Our method of using two different indicators as instance weights causes a complementary effect of combining the advantages of the two indicators.
3. When performing intelligent fault diagnosis, a lack of labeled target data is a prevalent situation. However, data from similar work environments are usually available. When one has few labeled target data, the proposed method and transfer learning can be an excellent alternative. From the comparison results, robust performance was found even when few data exist. Through this, it was confirmed that the applicability in actual industrial fields is promising.
# Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays
## 2 System Models  
As illustrated in Figure 1, we consider a uniform linear localized array composed of \(M\) subarrays, each with \(N\) evenly spaced phase-tunable antenna elements. Assume the arriving information-bearing signal \(\tilde{s}(t)\) with wavelength \(\lambda\) and elevation angle \(\theta\). The received signals at the \(m\)th subarray (\(m=0,...,M-1\)) are combined after phase shifting, and then the analog beamformed signal is down-converted to baseband. Through analog-to-digital conversions, the output signal is given by [5]  
\[s_{m}(t)=\tilde{s}(t)P_{m}(u,t)e^{imNu}+\xi_{m}(t), \tag{1}\]  
where \(\xi_{m}(t)\) is the zero-mean additive white Gaussian noise (AWGN) at the output of the \(m\)th subarray with power \(\sigma_{n}^{2}\); \(P_{m}(u,t)\) is the radiation pattern of the \(m\)th subarray at time \(t\) given by  
\[P_{m}(u,t)=\sum_{n=0}^{N-1}\tilde{P}_{m}^{n}(u)e^{i(mu+\alpha_{m}^{n}(t))}, \tag{2}\]where \(\bar{P}_{m}^{n}(u)\) denotes the radiation pattern of the \(n\)th antenna element (\(n=0,...,N-1\)) at the \(m\)th subarray. As in [4; 5], we assume \(\bar{P}_{m}^{n}(u)=1\); \(\alpha_{m}^{n}(t)\) represents the phase shift of the corresponding antenna element at time \(t\) and \(u=\frac{2\pi}{\lambda}d\sin\theta\).  
Let \(\rho_{m}(t)\) denote the cross-correlation between the output signals of the \(m\)th and (\(m+1\))th subarrays given by  
\[\rho_{m}(t)= s_{m}^{*}(t)s_{m+1}(t)\] \[= |\bar{s}(t)|^{2}P_{m}^{*}(\mu,t)P_{m+1}(u,t)e^{jNu}+s^{*}(t)P_{m}^ {*}(u,t)e^{-jmNu}\xi_{m+1}(t)\] \[+\bar{s}(t)P_{m+1}(u,t)e^{j(m+1)Nu}\xi_{m}^{*}(t)+\xi_{m}^{*}(t) \xi_{m+1}(t)\] \[= |\bar{s}(t)|^{2}P_{m}^{*}(\mu,t)P_{m+1}(u,t)e^{jNu}+z_{m}(t), \tag{3}\]  
where \((\cdot)^{*}\) and \(|(\cdot)|\) represent the conjugate and absolute value of \((\cdot)\), respectively; \(z_{m}(t)\) is approximated as an AWGN.  
In [3], identical phase shifts are used in all the subarrays, i.e., for any \(m\), the values of \(\alpha_{m}^{0}(t)\),..., \(\alpha_{m}^{N-1}(t)\) form the same arithmetic progression, such that \(Nu\) in (3) can be estimated by taking the argument of \(\rho_{m}(t)\). However, since \(Nu\) can be outside the range \([-\pi,\pi)\), the determination of \(u\) from the estimate of \(Nu\) (\(\bar{Nu}\)) will lead to phase ambiguity, i.e., there are \(2\lfloor N/2\rfloor+1\) possible estimates of \(u\), given by \(\hat{u}(p)=\frac{2\pi p+\bar{Nu}}{N}\), \(p=-\lfloor N/2\rfloor,-\lfloor N/2\rfloor+1,...,\lfloor N/2\rfloor\), where \(\lfloor\cdot\rfloor\) denotes the floor function. As a result, all possible directions need to be tested by applying a scanning beam within a long scanning frame, in order to find the one with the largest signal power, and thus incurring excessive delays.
## 3 Investigation of Scalability and Impact of Virtualization on Performance of the System
### Results Analysis
#### 3.2.3 Influence of Containerization on Performance  
Subsequently we investigated the influence of containerization on performance. For this comparison we used only two strongest configurations. One was using cores 0-19 and the second one was running on all available cores 0-39. In Figures 7 and 8 we can see differences in processing times measured on the server side and same measured on the client side. We notice that there is no significant difference between the containerized and bare-metal environments. We can conclude that under small load there is quite better performance on bare-metal but with growing load this difference diminishes (Figure 7). Times measured on the client side (Figure 8) indicate that even though small differences were found on the server side, the whole request processing time including communication, network load made the difference much less significant.  
Figure 5: Processing time of request-response process from client side—container, bars denote medians and whiskers Q1 and Q3 quartiles.