esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bending Test at 16 mm  
#### 3.3.1 Bending Test  
At this point, the structures that were previously selected as the best, lozenge and roller, have been subjected to further stress cycles, with particular attention to elastic return and shape recovery. In this phase, each structure was subjected to three cycles, carried out at close time intervals, that is, on the same day. Each new flex-elastic return-shape recovery cycle was performed after leaving the specimens at room temperature for about an hour, once removed from the tank to allow completion of the shape recovery. In Figure 12, the force-displacement graphs are shown for the lozenge and roller structures, for each  
Figure 11: Honeycomb structure after post-cyclic testing with breakdown detail.  
Figure 10: Energy absorption bending test at 16 mm.  
individual cycle. As regards the lozenge, the curve pattern is very similar from the first to the third cycle, with variations in the maximum load of 37% between the first and third cycles. Between the first and second cycles, the variation is 26%. The curve is characterized by a peak, to present a subsequent descending stretch, and then a stretch with an almost constant load is noted. For the roller structure, the force-displacement curve pattern shows a good degree of replicability between cycles, with the maximum load being reduced from 24 N to 15 N between the first and third cycle.  
The force-displacement curve of the first cycle for each structure is similar to that observed in single-cycle tests. Another interesting consideration is that the trends of the second and third cycles, for each structure, are very similar--trends which, in the case of the roller, do not deviate much from the curve of the first cycle, unlike the lozenge, where a greater difference can be noticed. In short, the lozenge has the highest mechanical strength with maximum load compared to the roller. Considering the second replication, the results of the force-deflection curves are consistent with those obtained for the first replication. The lozenge structure is confirmed as the structure capable of reaching the highest values in terms of stress, while the roller has slightly lower maximum loads during the three bending cycles. The observed cyclic bending behavior, especially for the lozenge structure, aligns with prior studies on patterned PLA cores.  
Ref. [54] showed that foam-filled composite lattice structures with lozenge cores exhibit decreasing peak loads across cycles, similar to our observations. However, our roller structure showed more stable behavior, suggesting an advantage in repeatability for less complex cell shapes. This is in agreement with reports from [55] on optimized curved-core lattice patterns.
## 5 Case Study (Results (Step 2 to 5 in Figure 3))
### Gap of MWL in Each Factor (Step 4 in Figure 3)
#### 5.2.3 Navigational Condition  
Table 13 shows the average weighted MWL of interviewees (\(n=10\)) depending on navigational condition. As described in Section 4.2, two typical manning cases are applied; every navigation staff is on board a ship (MO-1) and at RCC (MO-4). Rough video is displayed on the screen at RCC (LoDT-2 in Table 5). The ship is assumed to have an autonomous navigation system (LoA-3). Figure 8 shows the scenarios considered to have a significant difference between navigational conditions in the same manning option (\(p<0.05\) of t-test). Cells (Scenarios) at a start point and an endpoint of an arrow line in the figure have a significant difference. Table 14 shows the difference of MWL in the scenarios extracted in Figure 8. Table 15 breaks down the difference into six sub-elements and describes the sub-elements considered to have a significant difference between them (\(p<0.05\) of \(t\)-test). These suggest the following findings:  
* MWL significantly increases when the visibility is restricted due to weather conditions (NC-3 and NC-4) compared to the clear visibility (NC-1 and NC-2), even if the ships have an autonomous navigation system (LoA-3) ((1) to (6), (8) and (9) in Figure 8 and Table 14).
* MWL is quite high when traffic condition is severest in the channel in bad weather (NC-4). The responsible operators (ROs) especially feel the highest mental stress in all 25 scenarios of the study when they are at RCC (Table 13).
* Responsible operators feel more time pressure in the congested channel (NC-4) than navigating in the other areas when they are at RCC (MO-4) ((5)-(7) in Table 15). Notwithstanding, mental demands and frustration are also the primary sources of a significant increase in MWL in general (Table 15).
## 1 Introduction
### SLM and EBM Differences  
In this section, the SLM and EBM processes are discussed and their differences highlighted. In both processes, a layer of powder, which is a few tens of microns thick, is distributed from the feed container onto the build platform using a precision \(Z\)-axis positioner. The laser or electron beams operate on the \(X\)-\(Y\) plane of the powder surface. Powder distribution is controlled by the programmed information derived from the analysis of the sliced layers in the 3D computer model. After melting each powder layer, a positioner table is moved down; another powder layer is distributed and then melted by the laser or electron beams. The process is terminated when a complete 3D element is formed. After cooling, the component is removed directly from the building platform or after the stress relief heat-treatment [32; 33].  
The SLM (Figure 1a) and EBM (Figure 1b) machines are built in a similar way. Their most important parts are: an object building platform, a feed container, a deposition unit and a beam source, which is the main difference between the machines. SLM machines use a CO\({}_{2}\) (10.6-\(\upmu\)m wavelength) or Nd:YAG (1.06-\(\upmu\)m wavelength) laser with power between 50 and 200 W. EBM machines use a heated tungsten filament to generate an electron beam with the power between 50 and 3500 W. To reduce temperature gradients during EBM processes, initial sample preheating is applied by defocused electrons [34]. There is also the possibility of preheating in SLM systems. This works either by setting an additional laser to preheat the powder to temperatures below its melting point [35] or to adjust the building table temperature to a commercially-available maximum of 300 \({}^{\circ}\)C [36]. SLM machines use argon atmosphere to prevent sample oxidation, but there is a possibility to perform a process in a controlled atmosphere to enhance the mechanical properties of fabricated material [37]. EBM machines in contrast to SLM operate under vacuum. This is primarily required to prevent energy dissipation before delivering the beam to the part. The principal process parameters in all powder in bed methods are: the beam power and distance between beam vectors (hatch spacing) [38; 39], the scan speed and the scanning strategy [40; 41] and the thickness of the produced layers [42]. In contrast to EBM during SLM fabrication, there is a possibility to set scanning speed by two independent parameters: the point distance and the exposure time. In SLM machines, the laser does not focus continuously, but in the pulse mode. Therefore, the scanning speed is set by a distance between points where the laser is focusing and melting a powder (the point distance) for a short period of time (the exposure time). Table 1 presents a comparison between the Realizer SLM50 and the Arcam EBM S12 machines, which were used in this study for Ti-6 Al-4V processing.  
Figure 1: The schemes of (**a**) Selective Laser Melting (SLM) and (**b**) Electron Beam Melting (EBM) machines; 1, an object building platform; 2, a feed container; 3, a deposition unit; 4, a power source; 5, an argon flow nozzle.
## 3 Prototype LADAR Receiver Implementation  
The required transimpedance gain of the TICA is calculated from its minimum input current. For a lens of diameter 40 mm in the LADAR receiver and a laser peak power of 5 kW in the LADAR transmitter, the calculated returned laser power is approximately 1.5-11.5 uW for a maximum detectable range of 100 m. Here, the returned laser power is calculated using Ref. [12]. This value corresponds to the minimum input optical current of 9-69 uA when an avalanche photodiode (APD) responsivity is approximately 6 A/W. Thus, a minimum input current of 5 uA is targeted for the proposed TICA in order to have sufficient margin, and then the targeted maximum input-referred rms current noise is calculated as 0.5 uA when the signal-to-noise ratio is 10 [13; 14]. For signal processing in the LADAR receiver, a timing comparator generating the arrival time of the returned laser signal is used next to the TICA [7], which is implemented outside the prototype chip in this work. Considering the hysteresis of the timing comparator for noise immunity, the target minimum output voltage of the TICA has to be above 15 mV for a minimum input current of 5 uA. Thus, in this work, the transimpedance gain of the TICA is targeted to be approximately 70 dB-\(\Omega\).  
As each input channel of the TICA is the same as the TIA, the bandwidth of the TICA converges to that of the TIA. The required bandwidth (BW) of TIA can be calculated from Ref. [4] as follows:  
\[\text{BW}\;\cong\;\frac{0.35}{t_{r}}, \tag{2}\]  
Figure 6: Power consumption and bandwidth according to the number of combining input current buffers.  
where \(t_{r}\) is the rise time of the input optical pulse. In this work, the full width at half maximum (FWHM) of the input pulse is approximately 3.8 ns and its rise time is approximately 1.5 ns. The targeted bandwidth is approximately 230 MHz.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 2 Properties and Mechanisms of MNs
### Role in Cavitation and Emulsion Formation
The process removed 51% of the chemical oxygen demand (COD) in 60 min under ideal conditions (pH 3.5, oxygen flow rate of 20 mL/min, current density of 55 mA/cm\({}^{2}\), and initial phenol concentration of 100 mg/L). This was greater than the 29% removal achieved by the traditional electro-Fenton process. This novel approach provided a straightforward, economical, and eco-friendly solution, making it suitable for industrial electro-Fenton system applications. The study of natural and artificial nanomaterials and their usage, characterization methods, and ecosystem integration are the main areas of interest in environmental nanotechnology. The dimensions of MBs, micro-NBs (MNBs), and NBs vary; MBs are between 1 and 100 \(\upmu\)m, MNBs are between 100 nm and 100 \(\upmu\)m, and NBs are smaller than 1 \(\upmu\)m. These bubbles have special qualities, such as the capacity to produce free radicals, which makes them useful in a variety of industries, including wastewater treatment, agriculture, and food science [41]. Considering their numerous functional qualities, MB and NB technologies are utilized in the food and wastewater treatment industries, among other sectors, to improve the quality, cost, and efficiency of manufacturing processes.
## 3 Methodology
### Statistical Metrics  
The statistical metrics used in this study to assess the performance of the proposed approach are as follows:  
_Drip irrigation_: Small amounts of water are applied frequently resulting in smooth SSM dynamics making difficult to retrieve daily irrigation values [32]. For the flood irrigation, large amounts of water, of 60 mm on average, are applied every 2 to 3 weeks. This leads to a strong dynamic of soil moisture and contrasted soil moisture conditions that are easier to catch with a SSM observations every 6 days than the much smaller amounts applied using the drip irrigation technique that maintain soil moisture at high level. For this reason, the performance of the algorithm on the dripped sites is evaluated by accumulating irrigation amounts by 15 days following [35]. Statistical metrics including R, RMSE and bias are therefore computed between actual (true) and estimated 15-day cumulative amounts.  
_Flood irrigation_: The metrics computed for the flooding technique are intended for a daily evaluation of the irrigation estimate. The percent bias (Pbias) is calculated to measure the over/under-estimation in terms of amounts. The Total Positive Rate (TruPosRat) is the ratio of the number of irrigations detected within a specified range from actual irrigation (time difference from the actual events) to the number of actual irrigations events over the season. Here, the 4-day range was chosen as the 3-day range provided with much lower statistical metrics while the 5-day range did not improve significantly the scores (not shown). TruPosRat allows evaluating the irrigation estimates in terms of the timing (day of detection). Finally, in order to evaluate the underestimation or overestimation in terms of the number of events, the ratio between the estimated number of irrigation events and the actual number of events is computed (named irrigation events ratio and abbreviated to IrrigEvntRat hereafter).  
Finally, the estimated seasonal amounts (sum) of each plots and seasons, including the rainfed seasons, are compared to the measurements.
# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
## 3 Physical Layer Security Technology Based on Coded FTN Signaling  
In coded FTN signaling, specific code values are allocated to the \(\tau\). Consequently, a different \(\tau\) value is applied to each symbol. In this Section, three coded FTN signaling methods are introduced, considering physical layer security, complexity of signal processing, and bit error rate (BER) performance.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 3 Methodology: Selected Query DETR (SQ-DETR)
### Model Architecture and Layer-Wise Query Selection  
#### 3.1.1 Backbone and Encoder  
Given an input image \(I\in\mathbb{R}^{H\times W\times 3}\), a backbone network \(\phi\) extracts \(n\) multi-scale feature maps,  
\[F_{i}=\phi(I),\qquad i\in\{1,\ldots,n\},\ F_{i}\in\mathbb{R}^{H_{i}\times W_{i} \times C_{i}}, \tag{1}\]  
where \((H_{i},W_{i},C_{i})\) denote the spatial size and channel dimension of the \(i\)-th map. With a ResNet [27] backbone, we obtain \(n=4\) scales. To enrich semantics, we adopt the hybrid encoder of RT-DETR [5]; the deepest map \(F_{n}\) passes through a Transformer to inject global context. The result is fused with \(\{F_{1},\ldots,F_{n-1}\}\) via a Path Aggregation Network (PaNet) [26],  
\[E=\text{PaNet}\big{(}F_{1},\ldots,F_{n-1},\text{Transformer}(F_{n})\big{)}. \tag{2}\]
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Depth-Based Layering MAC (DL MAC)
#### 4.2.2. _Hight-Based Protocols_  
A protocol called the energy efficient, reliable, and cluster-based adaptive MAC (ERCA-AC) was proposed in [69] to improve the network stability and helps in extending UWSNs life. This protocol separates networks into medium-sized clusters, based on the number of sensor nodes, to avoid collisions of data packets transmitted, to complete a task at hand. For communication between nodes in UWSNs, this protocol employs the TDMA mechanism, which makes it capable of dealing with the problem of hidden terminal nodes in a cluster of sensor nodes mentioned earlier [92]. This protocol also aids in making the networks throughput better and reducing propagation delay [13; 92].
# Object Detection-Based Video Compression
## 3 Proposed Methods
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Random Access (RA)**} & \multicolumn{4}{c}{**All Intra (AI)**} \\ \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Sequence**} & \multirow{2}{*}{**QP**} & **Average** & **Average** & **Average** & **Average** & **Average** & **Average** \\  & & & \(\Delta\)**-PSNR** & \(\Delta\)**Cb-PSNR** & \(\Delta\)**Cr-PSNR** & \(\Delta\)**Y-PSNR** & \(\Delta\)**Cb-PSNR** & \(\Delta\)**Cr-PSNR** \\ \hline \multirow{5}{*}{**Class A1**} & \multirow{5}{*}{**Tango2**} & **22** & 0.05\% & \(-0.04\%\) & 0.07\% & 0.23\% & 0.04\% & 0.10\% \\  & & **27** & 0.03\% & 0.01\% & 0.09\% & 0.08\% & \(-0.02\%\) & 0.07\% \\  & & **32** & 0.08\% & 0.13\% & 0.08\% & 0.10\% & \(-0.03\%\) & 0.01\% \\  & & **37** & 0.12\% & 0.30\% & 0.12\% & 0.13\% & 0.02\% & 0.10\% \\ \cline{2-8}  & & **22** & 0.54\% & 0.57\% & 0.42\% & 0.08\% & 0.08\% & 0.08\% \\  & & **27** & 0.61\% & 0.58\% & 0.46\% & 0.08\% & 0.10\% & 0.14\% \\
4K & & **32** & 0.70\% & 0.69\% & 0.60\% & 0.13\% & 0.10\% & 0.15\% \\  & & **37** & 0.79\% & 0.70\% & 0.55\% & 0.22\% & 0.17\% & 0.22\% \\ \cline{2-8}  & & **22** & \(\mathbf{-1.22\%}\) & 2.43\% & \(\mathbf{-0.65\%}\) & 0.65\% & 0.32\% & 0.10\% \\  & & **27** & \(\mathbf{-0.95\%}\) & 3.78\% & 0.14\% & 0.22\% & 0.22\% & 0.12\% \\  & & **32** & \(\mathbf{-0.18\%}\) & 4.74\% & 0.65\% & 0.08\% & 0.15\% & 0.04\% \\  & & **37** & 0.80\% & 5.13\% & 0.94\% & 0.12\% & 0.06\% & 0.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the average \(\Delta\)Y-PSNR, the average \(\Delta\)Cb-PSNR, and the average \(\Delta\)Cr-PSNR for class (A1 to C) sequences.
# Cladding-Pumped Erbium/Ytterbium Co-Doped Fiber Amplifier for C-Band Operation in Optical Networks
## 2 Experimental Setup and Principles
Although this process may include a certain error due to the ambiguous edge detection of the fiber's core and inner cladding [28], it has an insignificant impact on the estimated overlap factor. When all edges are identified, an RGB color is assigned to each segment and they are recolored (see Figure 2b). The number of red (outer cladding), green (inner cladding), and blue (core) pixels are counted and used for the estimations. For our EYDF, \(A_{c}=658\) pixels, \(A_{icl}=112,828\) pixels, and the outer cladding area \(A_{od}=122,598\) pixels, which gives us \(\Gamma=0.0058\) and \(A_{icl}/A_{ocl}=0.9203\). Both these parameters are further used as input to the simulation setup.
## 5 Conclusions  
The running vibration of the SBG transmission is out of control under high speed and heavy load, which shortens the time between overhauls of the main reducer of helicopters and affects the service efficiency of helicopters. For this purpose, the active precontrol strategy for shape and performance was developed for the SBG transmission so as to extend the reliable service period of helicopter.  
Figure 16: Vibration displacement with lumped mass method.  
Figure 17: Vibration velocity with lumped mass method.  
Figure 18: Vibration acceleration with structural dynamics.  
1. The MS, LTE and MI are the main internal excitation sources of gears vibration. Combined with TCA, LTCA and MI theory, the MS, LTE and MI of gears are obtained. Based on lumped mass method, the dynamic model is built, and the gear dynamic performance is analysed by solving the differential equation of motion using a numerical method. An optimization model aimed at reducing the RMA of the SBG transmission is established with ease-off, and the redesigned SBG transmission with good dynamic performance is obtained through optimization.
2. The LTCA results show that the MS and LTE amplitude of the optimized gear is significantly lower than those of the preset gear, and their accuracy has also been verified using the FEA method. The impact force and impact velocity of the optimized gear are 27.02% and 25.81% lower than the preset gear. Therefore, the meshing quality of the optimized gears is obviously improved.
3. The results of dynamic analysis based on the lumped mass method show that the dynamic load factor of the optimized gears is much lower than that of the preset gear. The RMA value of the optimized gear is 76.91% lower than that of the preset gear, and they are all validated by the structural dynamics method. The normal vibration velocity and displacement of the optimized gears are obviously lower than the preset gears. Therefore, the running vibration of the gears is cut down after optimization, and their dynamic quality is improved.  
Therefore, the active precontrol strategy for shape and performance can obviously improve the dynamic quality of the SBG transmissions. It can also be used for the design and analysis of other gears.  
Conceptualization, [PERSON] and X.H. ([PERSON]); data curation, [PERSON]; formal analysis, Y.M.; funding acquisition, [PERSON] and X.H. ([PERSON]); methodology, Y.M. and X.H. ([PERSON]); resources, X.H. ([PERSON]); supervision, X.H. ([PERSON]); visualization, X.H. ([PERSON]); writing--original draft, [PERSON] and X.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The National Key Research and Development Program of China (No. 2020 YFB2008100). The National Science Foundation of China (No. 52205056).  
The study did not require ethical approval.  
The study did not involve humans.  
Data Availability Statement: Data is unavailable due to privacy or ethical restrictions.  
We thank all reviewers and editors for their valuable comments and suggestions.  
The authors declare no conflict of interest.
## 3 Methodology
### Build Prediction Methods
#### 3.5.2. Gated Recurrent Unit (GRU)  
The GRU are similar to the LSTM; they were created to solve the vanishing gradient problem; for this, they use two gates (restart and update) instead of the three gates that LSTM uses. The reset gate is used to determine how much information from the past is forgotten, and the update gate decides what information to discard and what new information to add. In the GRU structure, the reset gate \(r\) selects information from the previous state \(h\) to be discarded, and the update gate \(z\) selects new information from the input vector and the previous state \(h\) to be added to a new state. The candidate for the future hidden state is found by the candidate state gate \(g\)[30]. Compared with an LSTM-based model, a GRU-based model has a simpler structure and fewer tensor operations (about 25% less) due to the fusion of the cell state and the hidden state, making it easier to train the model and making it a very suitable candidate for integrated implementations [31].
## 3 Analysis of the Posture Adjustment Range
### Calculation of Workspace Volume  
Whenever a workspace boundary point is obtained from a search, the volume of the sub-workspace in which the point is located is obtained.  
In order to simplify the writing of the algorithm program, the polar coordinate system is selected instead of the original right-angle coordinate system, as shown in Figure 7. The coordinates of the reference point P under the original coordinate system as \((x,y,z)\) are converted to \((\rho\cos\theta,\rho\sin\theta,z)\) by the polar coordinate coordinates.  
The search range is divided into \(n\) equal parts parallel to the _xoy_ plane in steps of \(\triangle z=(z_{\text{max}}-z_{\text{min}})/n\).  
When the \(i\)-th search is carried out in the \(z=z_{j}\) plane, start from \(\theta=0^{\circ},\rho=0\), and gradually increase \(\triangle\rho\) until \(\rho=50 mm\); then, increase \(\triangle\theta\) until \(\theta=2\pi\), completing the search in this plane. The volume of its sub-workspace is:  
\[V_{ij}=\frac{1}{2}\Delta\theta\rho_{i}^{2}\Delta z \tag{9}\]  
Increase \(\triangle z\) and repeat the above steps to complete the search for all boundary points. The volume of the workspace is the sum of the volumes of all the sub-workspaces.  
\[V_{\text{a}workspace}=\sum V_{ij}=\sum_{i=1}^{i_{\text{max}}}\sum_{j=1}^{j_{ \text{max}}}\left(\frac{1}{2}\Delta\theta\rho_{i}^{2}\Delta z\right),i_{ \text{max}}=\frac{2\pi}{\Delta\theta},j_{\text{max}}=\frac{z_{\text{max}}-z_ {0}}{\Delta z} \tag{10}\]