esa-sceva/satcom-chunk-collection huggingface dataset:
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 3 Experimental Results
### Watermarking Experiments  
The first experiment was designed to test the encoder. The input models are originally expressed in STL format. The encoder converted the input models into voxel models at first. Then, it transformed these voxel models into distance fields and embedded watermarks there. The watermarked models are shown in Figure 6, including a tetrapod, a bowl, and a mug. The string, \"NTOU\", was served as the watermarks. The results were produced by a volume-rendering subroutine. To high-light the watermarks, the model boundaries were shaded in transparent white color while the watermarks were rendered in opaque red color. We cut off a portion of the bowl by using a clipping plane to better illustrate its internal structures. Among these three models, the tetrapod possesses a complicated structure, and hence its watermark is twisted. On the other hand, the watermark in the mug suffers less distortion because of the mug's simple shape.  
Conventionally, watermarks are inserted in imperceptible positions to enhance security. In this experiment, we purposely embed the watermarks into large curvy spaces in the test models to evaluate the capability of our encoding procedure. As the resultant images show, the watermarks blend well with their host models. The watermarks originate from a flat 2D pattern and the ROIs are comprised with voxels, scattering in curvy distance levels. There are enormous geometric and topological impurities between these two types of media. The experimental results show that the SOM subroutine bridges the gaps and successfully inserts the watermark into these voxel models.  
Besides watermarking the test models, blank-and-white images of the watermarks are produced and recorded for authentication purpose. These watermark images are displayed in the upper row of Figure 7. The watermarks of the tetrapod and mug are rendered in the front view while the watermark of the bowl is imaged through the left upper corner of the AABB. After being watermarked, the digital models were converted into G-code programs by using the slicer. The resultant G-code programs would generate fingerprinted contents if they were interpreted by simulators or executed by 3D printers.  
Figure 6: Volume rendering images of the watermarked models, (**left**) a tetrapod, (**middle**) a bowl, (**right**) a mug. The watermarks are shaded in red color.
## 4 Results and Discussion
### Finite Element Simulation Analysis of System Vibration Reduction  
According to the previous theoretical analysis, the electromagnetic damping force was calculated for the vibration reduction analysis. Two kinds of electromagnetic damping force were obtained through the circuit simulation. Because the electromagnetic damping force was too large when the coil closed the switch, which would lead to unreliable results, the total electromagnetic damping force (four times the damping force of a single coil) during the energy acquisition was imported into ABAQUS for calculation. The concentrated load applied at a node in the middle span area at the bottom of the beam is shown in Figure 19.  
After the ABAQUS analysis, the vibration curve of a node in the middle span was extracted again. The comparison between the two acceleration and velocity curves is shown in Figures 20 and 21. The signals of the acceleration and velocity were irregular. There was no great difference between the vibration signals before and after the electromagnetic damping force was applied. In this paper, the effect of magnetic damping was evaluated by the signal energy that was time integral of signal squared over a period of time [26]. The energy of the aperiodic signal can be expressed as follows:  
\[E_{x}=\int_{t_{1}}^{t_{2}}\lvert x(t)\rvert^{2}dt \tag{18}\]  
The acceleration signal energies of the node before and after magnetic damping were \(3.1048\times 10^{8}\) and \(3.1044\times 10^{8}\), respectively. The velocity signal energies of the node before and after magnetic damping were \(5.9764\times 10^{2}\) and \(5.9750\times 10^{2}\), respectively, and the reduction rates of the vibration energy of the node of the acceleration and velocity curves were \(0.01\%\) and \(0.02\%\), which verifies the vibration reduction effect of the bridge distributed vibration reduction and energy acquisition system.  
Figure 19: Schematic diagram of electromagnetic damping force loading.  
Figure 18: The coil electromagnetic damping without the energy harvesting.
## 5 Experiment and Results
### Discussion  
The accuracy of the three-dimensional surface of the target is the basis for the accurate reconstruction of the 3D sound field, so the measurement error of the stereo camera affects the reconstruction area. The error between the point cloud and the real surface of the target includes the offsets on horizontal, vertical and depth directions. The depth of each pixel is calculated from the horizontal pixels of the two cameras, and this ensures that the horizontal error is smaller than the depth error. Although the accuracy of modern stereo cameras has reached a very high level, these errors may still need to be considered in some scenes with high accuracy requirements. At the same time, the error relationship between  
Figure 11: Calculated sound power level versus time-angle.  
the depth error and the actual distance is given in Equation (11). It is necessary to pay attention to these key camera parameters.  
Moreover, since the reconstruction of the three-dimensional sound field is limited on the surface of the target, the sound source inside the target cannot be located accurately. A manual adjustment on the depth of the surface may have to be undertaken.  
It is worth noting that the recognition area (both depth and field of view) of the combined method is limited by both the stereo camera and the beamforming systems. Taking the effective recognition depth in the experiment as an example, the recognizable depth of the camera is 0.2\(\sim\)10 m, and the recognition depth of the microphone array is 0.3\(\sim\)5 m, so the depth recognition range of the entire system is 0.3\(\sim\)5 m.  
This study uses conventional beamforming (CBF) to eventually locate the sound source. At present, many advanced algorithms have been proposed based on CBF to achieve higher precision in the positioning or better interference suppression. The CBF in the proposed fusion method can also be replaced with those methods to take advantage of them.  
Furthermore, the sound intensity scaling method used to estimate sound power in this study is derived mathematically based on the physical propagation model of the point sound source. It is not suitable for accurate sound power estimation of multiple sound sources or directional sound sources.  
In the practice, the camera can be built into the array to avoid manual measurement of errors between the camera and the array. At the same time, it will bring an advantage that no additional visual and acoustic registration is required when reconstructing the three-dimensional sound field distribution.
## 2 Literature Review
### VTC and DC Gain of Inverters  
For a general inverter, the VTC is used to describe the characteristic between the input node and the output node. When the input voltage is low, as the pull-down transistor is shut down and the pull-up transistor is turned on, the output voltage is high. Similarly, the output voltage is low when the input voltage is high, as indicated in Figure 2a. However, with a reduction in the supply voltage (VDD), the VTC exhibits a gradual flattening trend, like the curve of VDD = 20 mV in Figure 2a. This trend is caused by the deteriorating ratio between the on-current and the off-current, \(i_{on}\)/\(i_{off}\). With the reduction of VDD, \(i_{on}\) aggressively declines. When \(i_{on}\) is less than \(i_{off}\), the output voltage cannot flip with the change of input voltage and a flattening trend is formed.  
The DC gain of an inverter can be obtained by taking the derivative of the curves in Figure 2a, as shown in Figure 2b. The maximum DC gain occurs at the moment when the output voltage flips. This can be interpreted as follows. When the input voltage is low, the output is high as the pull-up transistor is on, so the DC gain is zero. Similarly, when the input voltage is high, the output is low as the pull-down transistor is on, so the DC gain is also zero. At the moment when the output voltage flips, the DC gain \(A\) of a general inverter can be written as in (1):  
\[A=\frac{g_{mN}+g_{mP}}{g_{dSN}+g_{dSP}} \tag{1}\]  
where \(g_{mN}\) and \(g_{mP}\) are the small-signal transconductance of N-type MOS (NMOS) and P-type MOS (PMOS) transistors, respectively. \(g_{dSN}\) and \(g_{dSP}\) are the small-signal output conductance of the NMOS and PMOS transistors, respectively. With the reduction of VDD, the DC gain aggressively declines, as indicated in Figure 2b. When VDD is 20 mV, the DC gain is much smaller than 1.  
Figure 2: Voltage transfer curve (VTC) and gain curves of an inverter with a reduction in supply voltage: (**a**) VTC; (**b**) DC gain curves.
## 2 Hayabusa2 and Asteroid 162173 Ryugu  
Hayakusa2 [2] is the second asteroid mission organized by Japan Aerospace Exploration Agency (JAXA), inherited from the Hayabusa mission [16] which visited and returned samples from S-type asteroid 25143 Itokawa. Hayabusa2 is to explore a C-type asteroid, which is considered to be a parent body of volatile-rich carbonaceous chondrite meteorites and a key target to investigate the origin and evolution of the early solar system and the possible source materials of building blocks of life [17].  
Ground-based and space-based observations [18] predicted that Ryugu is classified as a C-type asteroid in taxonomy, of rounded shape with a diameter of about 0.9 km, and with rotation in 7.63 h. It is basically dark material with geometric albedo below 0.045, and possibly covered with coarse regolith of centimeter-sized granules with average thermal inertia of 150 to 300 tiu. Before the arrival of the Hayabusa2 spacecraft at Ryugu, the asteroid was considered to be a desiccated carbonaceous chondrite meteorite after thermally and aqueously alteration, and the surface might be sedimented with coarse-grained regolith materials and dense boulders, as well as excavated by impact craters where the materials from the interior might be excavated and exposed.  
Hayakusa2 was launched on 3 December 2014 and arrived at the home position, 20 km earthward from Ryugu on 27 June 2018, and started remote sensing [19] using the optical navigation camera (ONC) [20], the laser altimeter (LIDAR) [21], the near infrared spectrometer (NIRS3) [22], and the TIR [1; 4] to characterize the asteroid regarding its shape, spin state, geomorphology, spectral and thermal properties, geologic features, and gravity. The state and characteristics of Ryugu before and after arrival was basically consistent, but the asteroid shape is double top-shaped, and the surface is not covered with regolith but with boulders, and no flat and smooth terrains larger than 10 m scale were found for easy and safe landing [4; 19; 20; 21; 22].
## 2 Materials and Methods  
### Data Preprocessing  
For our model we used a dataset of 446 TKA preoperative plans retrospectively collected. All cases were planned by a single surgeon based on an MRI scan of the patient's knee joint. MRI scans have as benefit the visualization of the knee bone, as well as cartilage which can aid the surgeon during 3D templating. The MRI scan protocol is described in Table 1. The scans were semi-automatically segmented (Mimics, Materialise, Belgium) by experienced conversion engineers. The segmented mask contains both the bone and the cartilage of femur. Next, these masks were converted to a triangulated surface mesh based on the marching cubes algorithm [25]. For each patient the corresponding femoral implant size planned by the surgeon was also available. All patients received a posterior-stabilized Zimmer-Biomet Vanguard implant, which has 10 possible sizes.  
Our group lasso method relies on meshes which have point correspondences and identical adjacency matrices. To achieve this, we employ a previously developed statistical shape model (SSM) which was created based on a training set of 524 MRI scans independent from the dataset used for training the hypergraph regularized group lasso [26]. The SSM was fitted to all bones in the dataset to obtain point correspondence. Next, a non-rigid surface registration is applied to register the fitted model to the target femur [27]. Finally the vertex coordinates of the registered femur meshes are stacked in a matrix \(\mathbf{X}\in\mathbb{R}^{N\times 3p}\), where \(N\) indicates the number of femur meshes in the dataset, and \(p\) the number of points in the mesh. Finally, all columns of the data matrix \(\mathbf{X}\) are scaled to have a mean of zero and a standard deviation of 1. The implant sizes are coded as an ordinal variable with a range from 1 to 10, represented by \(\mathbf{y}\). Based on a stratified split, 70% of the samples are used for creating the model and the remaining samples form the test set to measure the final accuracy of the model.
## 2 Materials and Methods
### OFDM Communication and Radar Sensing  
#### 2.3.1 OFDM Communication  
An OFDM multicarrier signal is regarded as a parallel stream of multiple single-carrier signals with orthogonal carrier waveforms, each modulated with different transmit data and expressed as  
\[s(t)=\sum_{\mu=o}^{N_{\text{sym}}-1}\sum_{n=0}^{N-1}a(\mu N_{c}+n)e^{j2\pi f_{ n}t}\text{rect}\big{(}\frac{t-\mu T_{OFDM}}{T_{OFDM}}\big{)}, \tag{12}\]  
where \(\mu\) represents the individual OFDM symbol index within the total amount of \(N_{\text{sym}}\) OFDM symbols, \(n\) denotes the individual subcarrier index from a total amount of \(N_{c}\) subcarriers, \(a\) is the complex modulation symbols, \(f_{n}\) is the individual subcarrier frequency, and \(\text{rect}(t)\) denotes the rectangular pulse function, which has a value of \(1\) within the interval \([-1/2,1/2]\) and \(0\) outside this interval [7,8].
## 4 Real Ship Experimental Verification
### Simulation and Experimental Result Comparison Analysis  
In this paper, a detailed comparison and analysis of the simulation and experimental results were conducted. When the shaft-driven generator in the ship's propulsion shaft system was not connected to the load, the transient torque simulation calculation results and experimental results for the rear intermediate shaft are shown in Figure 15. Since only the dynamic torque at the following rotational speed points was collected in the actual ship experiment, the peak torque corresponding to the rotational speed points in the full-speed simulation calculation was selected to evaluate the consistency and differences between the two. Through the comparative analysis in Figure 15, it can be seen that the vibration torque obtained by the simulation calculation using the classic lumped-parameter equivalent system model and the Newmark-\(\beta\) step-by-step integration method has a small error with the actual measured torque in front of and behind the motor, all less than 10%, but the error with the rear intermediate shaft measurement point is relatively large, up to nearly 20%. The source of this discrepancy may lie in the fact that the current simulations of the dynamic response of the propulsion shafting system have neglected the influence of the propeller's pulsating dynamic forces on the system's torsional vibrations. There is a minor discrepancy between the results of the dynamic response simulations and the measured torque data both upstream and downstream of the electric motor. However, both deviate considerably from the measured torque at the mid-shaft measurement point. This divergence is predominantly attributed to the mid-shaft measurement point's proximity to the propeller, which subjects it to a greater impact from the propeller's pulsating forces. Conversely, the measurement points located further away from the propeller, both upstream and downstream of the motor, exhibit measured data that are less affected by these pulsating forces.  
The transient torque simulation calculation results and experimental results for the propeller shaft when the shaft-driven generator in the ship's propulsion shaft system was connected to the load are shown in Table 4. Table 4 shows the simulated vibration torque of the propulsion shaft system and the actual measured torque at the midpoint of the intermediate shaft and the front and rear ends of the motor at a rotational speed of 53 r/min. The comparative analysis shows that the simulation torque results are less than 6% different from the actual measured torque results at different measurement points. Therefore, in actual ship navigation, the impact of the unsupported shaft-driven generator on the propulsion shaft system is small, which has a certain guiding significance for the later installation and design of the shaft-driven generator.  
Figure 15: Comparison of simulation and experimental results.
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
## 3 Results and Discussion
### Effect of the Air Vent Area on the Air Demand  
As shown in Figure 4, increasing the air vent area (\(a_{1},a_{2},a_{3}\)) leads to increasing air discharge (\(Q_{a}\)) and decreasing air velocity (\(U_{a}\)), as increasing the air vent area decreases the energy loss coefficient \(\delta\) of the air vent. The pressure (\(p\)) in the spillway tunnel decreases with increasing air vent area (\(a\)). The air pressure increases (from negative values towards zero) along the spillway tunnel and finally reaches atmospheric pressure at the spillway outlet, as shown in Figure 5. When the air vent connects to the tunnel, the pressure on the upstream side of the air vent is greater than that on the downstream side, which was verified by [PERSON]'s numerical results [15]. As shown in Figure 6, the value of the air discharge (\(Q_{a}\)) decreases in the order air vent #1, #2, #3 due to the decreasing pressure difference along the tunnel, and the increasing air vent area leads to an increasing percentage of air discharge in air vent #1 and a decreasing percentage of air discharge in air vents #2 and #3. Overall, it is essential to determine the optimal combination of air vent areas via the method described in Section 4.1 and to meet the air supply system requirements of spillway tunnels without causing engineering waste.  
Figure 4: Functional relationships between air flow characteristics (\(U_{a}\), \(Q_{a}\)) and air vent area (\(a\)).  
Figure 3: Air pressure \(p\) at the connection of the air vent and the tunnel under varying water flow conditions (\(U_{w}\) and \(h_{w}\)).
###### Abstract  
Fluorinated diamond-like carbon (F-DLC) coating is biologically safe, provides superior antibacterial properties, and shows promise in preventing postoperative peri-implant infections. However, potential negative effects of this coating on in vivo bone formation and resorption have not been studied. The authors investigated the effects of F-DLC coatings on bone union in beagle dogs. Seventy-two solid columns of titanium alloy were prepared with equally spaced slits. Half of these columns were coated with F-DLC (Group F), and the others were left uncoated as controls (Group C). Columns were implanted in the femurs of beagle dogs, and in vivo bone formation and resorption were assessed 4, 8, and 12 weeks after implantation. In comparison to Group C, Group F showed significantly greater bone volume and trabecular thickness at Week 8 (\(p<0.05\)) and Week 12 (\(p<0.005\)) and significantly lower bone resorption activity, measured by the ratio of osteoclasts to bone surface and of eroded surface to bone surface, at Week 12 (\(p<0.05\)). The F-DLC coating encouraged bone formation in vivo more effectively than uncoated titanium alloy, suggesting that F-DLC will prove to be a useful coating material for antibacterial intraarosseous implants.  
F-DLC coating; antibacterial properties; peri-implant infection; bone formation; bone resorption +
Footnote †: journal: Applied Sciences
## 4 Results--Case Study
### Analysis and Summary
* _Strengths_: at present, Company A benefits from a robust network of relationships with customers and suppliers (e.g., drivers), which was leveraged during the pandemic period to provide a rapid response to the increased request by the consumers. The company has also leveraged the usage of digital technologies, which made logistics activities more efficient and, again, allowed the company to respond to consumer demand in the pandemic period. * _Weaknesses_: Company A has suffered from low economic results, in particular in the post-COVID-19 period, mainly due to the high production costs. Efforts must be made by the company to reduce expenses. At the same time, however, the service level, in terms of delivery lead time or on-time delivery, should be safeguarded. * _Opportunities_: the growth of e-commerce, experienced in the COVID-19 period but expected to last over time, creates opportunities for increasing the volume of items handled by Company A. Indeed, the survey phase demonstrated that the company's consumers have shifted towards the usage of online sales; hence, the company could consider investing in this area to increase its market share. By leveraging the e-commerce logistics and diversifying service, expansions could also be possible at an  
Figure 11: Economic outcomes of Company A. Negative values have been highlighted in red. international level. Even if the company has already embraced the implementation of digital technologies, some emerging technologies (e.g., drones or advanced traceability systems) could also be introduced for further improving the logistics efficiency. Finally, sustainability is another opportunity to be leveraged, because of the current push towards the adoption of environmental-friendly logistics solutions. Examples of those solutions include a reduction in CO\({}_{2}\) emissions, and the usage of electric vehicles or zero-impact materials. * _Threats_: the growth of e-commerce can be seen as an opportunity, but because many logistics companies have already entered this field, the sector is characterized by very high competition, which could limit the market share of Company A; this could instead be seen as a threat needing to be properly managed. Another threat comes from the increased cost of fuel, which, for sure, for a logistics company plays an important role in determining the cost of the transport activities (also, having previously observed that the company suffered from a limited revenue in recent years). This factor could further push towards the adoption of environmentally friendly transport modes (e.g., electric vehicles), which have been previously mentioned as an opportunity for leveraging in the logistics sector.
## 5 Experimental Results and Analysis
### Dynamic Scheduling Parameters Analysis of Periodic RH Strategy
The results show that the periodic RH dynamic scheduling strategy is superior for solving large-scale carrier aircraft support scheduling problems, and the RH dynamic scheduling strategy, which closely approached an actual situation, was proven to be suitable for carrier aircraft scheduling.