esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
When a submarine encounters an emergency situation, it should take emergency-surfacing actions by moving upward with a large angle of attack in the vertical plane. Previous research has often neglected the effect of vertical plane motion on the lateral force (\(F_{y}\)), rolling moment (\(M_{x}\)), and yawing moment (\(M_{z}\)). To examine the flow characteristics of submarines at high angles of attack on the vertical plane, the SST-DDES method is adopted in conjunction with adaptive mesh refinement (AMR) technology, and the new Omega vortex detection method is employed as the AMR criterion for numerical calculations. The obtained results are then appropriately verified by conducting water tank experiments, and the effects of different angles of attack and heel angles on \(F_{y}\), \(M_{x}\), and \(M_{z}\) are methodically examined. The results reveal that, in the flow around the vertical plane of a submarine, the influence of \(F_{y}\) and \(M_{z}\) cannot be ignored. In addition, when the vertical velocity of the hull is greater than 0.6 m/s, the influence of \(M_{x}\) cannot be overlooked either. When the angle of attack on the vertical plane of the submarine is greater than 25\({}^{\circ}\), the effects of \(F_{y}\), \(M_{x}\), and \(M_{z}\) cannot be neglected, and the effect of \(M_{z}\) is particularly prominent, with its amplitude close to or greater than the average value of the pitch moment (\(M_{y}\)). The obtained results reveal that the presence of the heel angle (\(\theta\)) intensifies the forces on the hull for \(F_{y}\), \(M_{x}\), and \(M_{z}\), and the forces caused by the vertical velocity at \(F_{y}\), \(M_{x}\), and \(M_{z}\) cannot be neglected. These findings can provide a mechanical analysis basis for the analysis of nonlinear motion phenomena during submarine surfacing.  
submarine; adaptive mesh refinement; flow around the vertical plane; high attack angle; heel angle; experimental study +
Footnote †: journal: Applied Sciences
# Image Error Concealment Based on Deep Neural Network
## 4 Experiments
### Objective and Subjective Performance Comparison
As illustrated in Table 4, the proposed method gained the best performance under the average PSNR and SSIM. Specifically, compared with KMMSE, SLP, and AVC, the average PSNR gains were 0.32 dB, 1.19 dB, and 1.3 dB, respectively. In order to represent the performance of the proposed method, subjective comparisons are also given in Figures 19-21. As can be observed from the figures, the proposed method produced the most visually pleasant results among all compared methods. Figure 19 compares the performance of the proposed method with the others working under isolate block loss. Severe blocking artifacts were produced in POCS, AVC, CAD and VC, and a blurred and lumpy boundary could be observed in MRF and OAI. Figure 20 presents the comparison results on regular consecutive block losses, which has the high block loss rate. The POCS, CAD, and MRF produced very serious lumps. It also observed that the CAD and VC methods produced many false edges. Only the proposed method and the recent KMMSE method produced a natural reconstruction. Figure 21 presents the comparison results on random block losses; under this loss mode, the EC task is more challenging since many missing blocks may cluster together, making it difficult to find a regular and reliable neighborhood. It can be found that some loss pixels cannot be estimated very well. Only the proposed method can restore the major edges and textures.
# Investigation of Cement and Fly Ash on the Improvement of Fine Sand Soil
## 3 Results
### Falling Head Permeability Test  
Falling head permeability tests are conducted to determine the permeability coefficient of the samples, considering the materials (cement and fly ash) in the mixtures and the maximum grain size of the sand (D\({}_{\rm max}\) = 0.60 mm). The results of the tests are presented in Figure 6. The addition of fly ash and cement to the sand soil results in a reduction in the permeability coefficient of the samples at all levels. The pure sand sample (S) exhibits the highest permeability coefficient which is 9.99 \(\times\) 10\({}^{-4}\) cm/s, while the S3C25 FA sample shows the lowest value at 5.18 \(\times\) 10\({}^{-5}\) cm/s. This represents a decrease in the permeability coefficient of approximately 20 times compared to the pure sand. It may be because of the inclusion of fly ash and cement fills the gaps between the sand particles, creating a denser and void-free structure, thereby reducing permeability. In the study of [PERSON] et al. [32], it was indicated that C class fly ash permeability was less than 10\({}^{-6}\) cm/s; this could also be due to the decrease in the permeability through the increase in the amount of fly ash.  
Figure 5: CBR test results after 7 and 28 days curing.
# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 2 Materials and Methods
### GLASS GPP Data  
Global Land Surface Satellite (GLASS) gross primary production (GPP) is a global surface remote sensing product with a long time series and high precision based on multi-source remote sensing data and ground observation [45]. The Bayesian multi-algorithm integration method integrates eight widely used light use efficiency (LUE) models, including CASA, CFix, CFlux, EC-LUE, MODIS, VPM, VPRM, and two-leaf. The spatial and temporal resolution of GLASS GPP are 5 km and eight days, from 1982 to 2018. The unit of the data is gCm\({}^{-2}\)d\({}^{-1}\). The development and validation of the algorithm are based on the data from global fluxnet, which contains nine types of terrestrial ecosystems, such as evergreen broad-leaved forest, evergreen coniferous forest, deciduous broad-leaved forest, mixed forest, temperate grassland, tropical savanna, shrub, farmland, and tundra. GLASS GPP can be downloaded from [[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)) (accessed on 23 April 2023).
## 5 Discussion
### Limitation and Future Works  
#### 5.3.1 Discrepancies on the Running Parameter  
The tactics incorporated in this paper achieved running stability and reference speed by adjusting \(k\) and \(\theta\), while force control enabled the regulation of the energy state. We adjusted the weighting parameters to reproduce motion, focusing on matching stride and cadence. As a result, discrepancies increased in another motion parameter, amplitude. These discrepancies can be attributed to errors in both the dynamical model and the control model, suggesting that certain aspects of motion cannot be fully represented by the SLIP model. For instance, while the SLIP model assumes a single spring, the human body has multiple joints. Furthermore, this study experimentally incorporates only three strategies, meaning that not all tactics used by humans are included. As a result, achieving a perfect match across all motion parameters proved challenging. In the current simulation, the swing phase maintains a constant velocity in the direction of progression, meaning that the stride can be adjusted by modifying the vertical jump. Additionally, the implemented force control applies forces only in the axial direction without adjusting their orientation. In the model, stride is increased by applying greater propulsion forces, which in turn leads to excessive vertical jumping and, consequently, errors in amplitude.  
A possible future improvement is to introduce mechanisms that allow for differential energy distribution. The current force control system provides feedback on the overall energy state, where kinetic energy is modulated by stiffness control. However, potential energy is not explicitly controlled. Incorporating a strategy to regulate potential energy may improve the accuracy of motion reproduction.
# A Projection-Based Augmented Reality System for Medical Applications
## 2 Methodology
### Image Alignment  
In order to align the 3D model of the blood vessels, the real-world space coordinates and the CT image space coordinate must be aligned by calculating for the transformation matrix based on the two coordinate groups. In order to achieve this goal, an Iteration Closest Point (ICP) algorithm is used. The ICP method is based on the one proposed by [PERSON] [24], which aligns floating point data to reference point data, so that the conversion relationship between the two sets of data can be obtained and used to calculate the geometric transformation matrix. Based on the characteristics of the data, the determination of the initial point of search is important in this ICP algorithm. There is no guarantee of reaching the global optimum. So, a modification in the form of adding a perturbation mechanism should help the algorithm reach a better solution even if a random initial starting point is used. The modified ICP algorithm is as followed:  
1. Let the floating point group corresponding to the patient's facial features, extracted from the camera image, be \(F\), where \(F=\{f_{i}(x,y,z),1\leq i\leq N_{f}\}\), and let the floating data point group corresponding to the facial features, extracted from the CT images, be \(R\), where \(R=\{r_{j}(x,y,z),1\leq j\leq N_{r}\}\).
2. Pick a random point from \(F\), assume it is \(f\), and seek its closest corresponding point in \(R\) by calculating the minimum distance between \(f\) and \(R\), \(d\), as: \[d\ (f,\ R)=\frac{1}{M_{j}\in(1,...N)}\ \parallel f_{i}-r_{j}\parallel\] (1) where \(M\) is the normalizing constant.
3. Calculate the median distance, Median, of all the distances. Assume the distances are re-arranged in order, then: \[\text{Median}(d)=d_{j},\ \text{where}\ j=\left\{\begin{array}{l}\frac{N_{f}}{2},N_{f} \ \text{is}\ \text{odd}\\ \frac{N_{f}+1}{2},N_{f}\ \text{is}\ \text{even}\end{array}\right.\] (2)
4. Assign weight to each pair, based on the distance between each pair of points. \[W_{i}\ =\ \left\{\begin{array}{l}1,\ if\ d_{i}<median\\ \frac{median}{d_{i}},\ \text{otherwise}\end{array}\right.\] (3)  
Figure 3: Example of a 3D model of cranial blood vessels.  
Calculate the root mean squared error (RMS): \[\text{RMS}=\sqrt{\frac{1}{N_{f}}\sum_{i=1}^{N_{f}}W_{i}\cdot d_{i}}\] (4)
6. Calculate and record the transformation matrix, T, of current pairs. If the termination condition, based on RMS, is reached, output T as the final transformation matrix. However, if the termination condition is not reached, but the RMS value is smaller than the previous iteration, then replace the optimal transformation matrix with the current T.
7. If the termination condition is not reached, a perturbation mechanism is used; that is, applying a perturbation matrix on the current pairings. The probability of perturbation is based on the Gaussian distribution. The purpose of perturbation is allowing the search outside the current solution space, which may be small, and can allow for a locally optimum solution.
8. Restart the ICP again. The flowchart of the modified ICP algorithm is shown in Figure 4.
## 4 Parameters Design and Experimental Results
### Experimental Results
Although this negative current is very small, it is enough to release the parasitic capacitance of the MOSFET and allow the active switch to meet ZVS. Figure 9 shows the voltage and current waveforms of the two active switches. It can be seen from these experimental waveforms that before \(v_{GS1}\) (\(v_{GS2}\)) changes from low to high level, there is a negative current that discharges the parasitic capacitance of the active switch to zero volts. This allows the active switches to be turned on at zero voltage and zero current. Figure 10 shows the LED voltage and current waveforms. The measured LED power is 181 W, which is quite close to the design target values. The circuit efficiency is 92%. In addition, it can be seen that the LED voltage exhibits low-frequency ripples (120 Hz), and the ripple factor is calculated to be approximately equal to 7%. This ripple voltage can be reduced by using larger output capacitance or larger dc-link capacitance. Figure 11 shows the photo of the proposed LED driver test stand. Figure 6: Inductor currents of the PFC converters. (**a**) \(i_{p1}\) and \(i_{p2}\) for several input voltage cycles. (\(i_{p1}\), \(i_{p2}\): 2 A/div, time: 5 ms/div). (**b**) expanded waveforms of \(i_{p1}\) and \(i_{p2}\) when the input voltage is near the peak value. (\(i_{p1}\), \(i_{p2}\): 2 A/div, time: 5 us/div). Figure 8: Waveforms of leakage current of \(T_{1}\). (\(i_{IJ1}\), \(i_{IJ2}\): 0.5 A/div, time: 5 \(\upmu\)s/div). Figure 7: (a) Waveforms of the input voltage and current. (\(v_{in}\): 50 V/div, \(i_{in}\): 2 A/div, time: 5 ms/div), and (b) comparison between measured input current harmonics and IEC standard [6]. Figure 10: Waveforms of LED voltage and LED current. (\(V_{LED}\): 100 V/div, \(I_{LED}\): 1 A/div, time: 5 ms/div).
## 4 Discussion  
### Imaging of Renal Masses  
Except for simple renal cysts, which can be easily diagnosed by ultrasound, other renal masses need further investigation or follow-up. MR is considered the most accurate diagnostic tool but has several contraindications [31; 32], whereas CT involves exposure to ionizing radiation and iodinated contrast agents, presenting a potential risk of nephrotoxicity in patients with renal insufficiency. Furthermore, the main diagnostic limitation of both methods lies in their poor ability to adequately characterize small lesions for various reasons, including motion artifacts on MRI or the presence of pseudo enhancement in cystic lesions on CT [33; 34]. Alternatively, when performed by a skilled operator, CEUS proved to be a suitable alternative diagnostic tool, but it is not widely available and requires specific equipment and the collaboration of a second operator [5].
# Analytical and Measured Effects of Short and Heavy Rail Cars on Railway Bridges in the USA
## 2 Modeling and Analysis of a Steel Railroad Bridge
### Modeling and Analysis of a Steel Railroad Bridge Using FEM Model  
The modeling and analysis of railway bridges under moving load may be divided into two groups: a very simple vehicle model with an advanced bridge model, and a very complicated vehicle model with a simple bridge model. In addition to these two groups, some models include excitation due to rail surface roughness while other models neglect it. The inseparable part of railway bridge modeling is a consideration of the track and its properties; however, the full vehicle-track-bridge model is not always necessary to evaluate the complex bridge response under moving loads. The very detailed and complicated vehicle models are unnecessary if the main purpose is to study the bridge's structural response [9].  
The advantage of the FEM model concerning the evaluation of existing bridges is that the structural performance and its actual load-carrying capacity can better represent the field measurements. The current bridge condition and rate of deterioration reported during the technical inspection can be introduced to the FEM model. The defects and various bridge conditions can be incorporated in the FEM models to simulate and analyze the structural performance. The 3D FEM model of the existing bridge that contains all components and connections can predict stress concentration in places that are difficult to measure in the field. For example, for fatigue evaluation, the stress ranges obtained from measured strains are often not sufficient for cycle accumulation at the locations measured on the spans. The location of the maximum stress concentration and stress range can be identified using the FEM model. However, the advanced FEM models should be validated with field measurements to provide the most accurate bridge structural behavior. Moreover, the dynamic computations (simulations) are more reliable when the analytical inputs, including rolling stock data, are provided by the railway authorities [10].  
A real-time field data acquisition system is an important tool for obtaining the response of a structure under actual dynamic loading conditions. The results obtained from in-situ studies, with the whole range of operating and material conditions, are the most reliable [11]. The field data provides information about the actual behavior of the structure's capacity, the degree of load distribution by individual elements, and the identification of dynamic parameters. The development of measuring equipment and signal processing technology enables field testing to be carried out in an automated manner, which leads to the creation of technical monitoring systems [12; 13]. [PERSON] et al. [14] used strain gauges for monitoring the Soerstrom Bridge in central Stockholm to find out the remaining fatigue life of stringers and cross beams. The researchers also explored differences between the theoretical strain values and the obtained field strain data in predicting the initiation of fatigue crack in bridge members.
# Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections
## 2 Literature Review
### Abdominal Compression  
Respiratory motion management through the abdominal compression technique involves the application of pressure on the abdomen through compression plates. [PERSON] et al. [11] tested the effect of different degrees of abdominal compression on the movement of tumor in the lung. For no compression, medium compression and high compression, the average tumor motion was observed to be 13.6 mm, 8.3 mm and 7.2 mm, respectively. Although the overall movement can be reduced by applying abdominal pressure, this technique causes inconvenience to the patient undergoing the scan.
# Smart Low-Cost Control System for Fish Farm Facilities
## 4 Proposal for Smart Low-Cost Control Systems for Aquaculture
### Adaptations for Smart Low-Cost Control Systems for Aquaculture Facilities with RAS
#### 4.2.2 Sensors and Actuators  
In RAS, all the data must be obtained from sensors deployed in the tanks, their surroundings, and the different parts of the water reconditioning system. In this case, all the information from the sensors can be directly applied to evaluate the performance of different actuators. As in cages in the sea, the water temperature and water DO are the most important ones to predict fish performance. For fish and feed monitoring, the use of cameras and Light Dependent Resistances (LDRs) can be used. Cameras are used to detect the feed falling while LDRs are selected for fish swimming behavior monitoring [110].  
The actuators in RAS facilities include the feeders, part of the water reconditioning mechanisms, and acoustic and visual alarms in the monitored areas. As in the cage in the sea, in RAS facilities, the system should be able to operate automatically even if the connection with the DB is lost. In this case, the automatic operation includes feeding, aeration, and skimmer operation in the tanks, the full operation of the water reconditioned process, and triggering the alarms. The location of deployed sensors and sensor nodes can be seen in Figure 13.
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
## 2 Methods
### Assessment Methods of PPP-B2b Corrections
Since there are more than 120 global monitoring stations to calculate the GeoForschungZentrum (GFZ) multi-GNSS final product (GBM) using the B1I/B3I IF observations of a three-day arc, which can provide high accuracy of orbit and clock offset, the GBM final product is taken as the reference. With the GBM final products as a reference of comparison, the quality of the PPP-B2b orbit and clock offset products is assessed. The following issues must be considered. First, the PPP-B2b products provide satellite positions with the antenna phase center (APC) of B1I signal as a benchmark, while the GBM final product provides satellite positions in terms of the center of mass (CoM) [26]. Therefore, phase center offset (PCO) modifications are required to unify the satellite positions to the CoM using the \"igs14.ack\" file. It should be noted that the antenna file contains only the BDS-3 satellite antennas' PCO and not the phase center variation. Second, the PPP-B2b product takes the B3I signal as the reference for the BDS-3 satellite clock offset, while the GBM final clock offset product takes the B1I/B3I IF signal as the reference. Therefore, the PPP-B2b DCB should be used to unify the BDS-3 clock's time reference. However, for GPS satellites, both the PPP-B2b and GBM clock offset are determined against the L1/L2 IF combination signal; hence it does not need to be considered. Moreover, PPP-B2b and GBM products take the BDT and GPST as the reference, respectively. Thus, the time difference of 14 s between them should be eliminated.
## 3 Experimental Setup
### Data-to-Image Conversion
#### 3.4.6 Id-STFT Image Construction  
Time-frequency analysis using STFT was applied to the single Id current signal.  
The Id current time series consisted of 1024 samples, and the same STFT parameters used for the three-phase STFT were applied: a Hamming window, window length of 128, time step of 27, and 65 frequency bins.  
Figure 7 illustrates example images converted using the six methods described above.
## 6 Parametric Study and Discussions
### Effect of Cutting Surface Width  
Figure 16 illustrates the relations between interaction forces \(F_{x}\), \(F_{y}\), \(F_{z}\) and cutting surface width \(B\) under different eccentric distances of 0, 0.1 \(D\), 0.2 \(D\), 0.4 \(D\) and 0.6 \(D\), where \(D\) = 6 m is the diameter of the shield machine. It can be observed that with the increase of the cutting surface width, the interaction forces \(F_{x}\), \(F_{y}\) and \(F_{z}\) show an overall increasing trend with slight decreases in some ranges. The main reason is the increase of the number of tools. The interaction force \(F_{z}\) is much greater than \(F_{x}\) and \(F_{y}\), and increases significantly. Especially when the cutting surface width exceeds 1.0 m, the gap is more obvious. A possible reason is that when the pile foundation width is large, the tools at the opposite sides of the cutter head (upper side and lower side) have opposite cutting directions (in the X direction) and they partly cancel out cutting force of each other, while cutting forces in  
Figure 14: Additional torque at different cutting distances (pile No.1, \(e\) = 0.2 D).  
Figure 15: Additional torque at different cutting distances (pile No. 2, \(e\) = 0).  
the Y direction were added to each other and penetrate forces in the Z direction have the same signs as well.  
The relations between the moments \(M_{x}\), \(M_{y}\), \(M_{z}\) and cutting surface width \(B\) under different eccentric distances are shown in Figure 17. In a similar way as the interaction force, the moments increase with the width of the cutting surface, and the moment \(M_{z}\) increases more significantly. \(M_{z}\), which can be considered as the additional torque of the shield cutter head, is also much greater than \(M_{x}\) and \(M_{y}\), which are considered unbalanced moments. Therefore, it can be inferred that the adequate torque for the shield machine is required to ensure that the shield can cut piles and pass through quickly.  
Figure 16: Effect of the cutting surface width \(B\) on the interaction forces: (**a**) \(F_{x}\); (**b**) \(F_{y}\) and (**c**) \(F_{z}\).  
Figure 17: _Cont._