esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Results Analysis
#### 4.4.5 Reality Testing  
In order to evaluate the prediction effect of the classification model based on Triple-EfficientNet proposed in this study, seven rock images of various types are randomly selected from the rock dataset for prediction, and the prediction probability of seven rock-type labels in each image is output, as shown in Figure 15. The rock-type label corresponding to the maximum predicted probability value is the final classification result. The results show that the classification effect of this model on all kinds of selected images achieved a high level, with an accuracy of more than 95%, and the prediction probability of black coal, dark gray silty mudstone, and gray argillaceous siltstone is close to 100%. The results show that the classification model proposed in this study has great robustness and generalization ability.
# Na-Doping Effects on Thermoelectric Properties of Cu\({}_{2-x}\)Se Nanoplates
## 2 Materials and Methods
### Synthesis of Na-Doped Cu\({}_{2-x}\)Se Nanoplate  
Sodium was incorporated via the following step: the as-synthesized Cu\({}_{2-x}\)Se nanoplate (2.6 g) were reacted with sodium hydroxide-ethylene glycol solution (3.1 M) under vigorous stirring at 413 K for 24 h. Finally, the products were collected by centrifugation after a number of washing procedures with acetone and ethanol.
# Accuracy Analysis of GNSS Hourly Ultra-Rapid Orbit and Clock Products from SHAO AC of iGMAS
## 3 Assessment and Analysis  
In order to assess the CSPT method of SHAO AC for hourly products, the computational efficiency of the 6-hourly and 1-hourly procedures are compared with each other. The 6H and 1H will be used on behalf of the 6-hourly and 1-hourly products in this study, respectively. Subsequently, the accuracy of different orbit prediction methods is discussed in this study. Finally, the accuracy of hourly orbit and clock products is analyzed by being compared with reference products from IGS/MGEX products.
## 3 Experiment and Results
### Repeatability of Maximal Cross-Sectional Profile Measurement  
The maximal cross-sectional profile of the ruby microsphere is measured to verify the feasibility of the proposed method. Figure 12 illustrates a group of measuring data (solid triangle) and its fitted data (blue solid dot).  
Figure 11: The noise level of probes: (**a**) Noise level of probe-1; (**b**) Noise level of probe-2.  
Figure 10: Approaching curves: (**a**) Approaching curve of probe-1; (**b**) Approaching curve of probe-2.  
The data from the two probes are fitted separately by the least-squares method to obtain their respective centers. A coordinate transformation is conducted to make the centers coincide with each other. Thus, the data from the two probes can be integrated with a common center. Then this center is moved to the origin of the coordinate, and the least-squares fitting is implemented for the whole data to acquire the final result of the cross-sectional profile circle. Normally, the shape of the cross section circle is very smooth, and the error in the integration process of the two centers is very small and can be neglected. The experiments are repeatedly performed (four times) to verify stability and repeatability (Figure 13). The standard deviation of the repeatability of a single measurement position is 15.1 nm.
## 3 Materials and Methods
### N-glycan Analysis by MALDI-ToF Mass Spectrometry  
A microflex MALDI-ToF mass spectrometer (Bruker Daltonics GmbH, Bremen, Germany) was used. Ten \(\upmu\)L of purified \(N\)-glycan samples were spiked with five \(\upmu\)L 100 mg/L of the human milk oligosaccharidesacharide 3'-fucosyllactose (3-FL, Dextra Laboratories Inc., Reading, UK) as an internal standard. A one \(\upmu\)L aliquot of spiked sample was combined with an equal volume of 2,5-dihydroxybenzoic acid matrix (20 mg/mL in 30% acetonitrile, 0.1% trifluoroacetic acid) and 0.4 \(\upmu\)L 1 mM NaCl was addedto achieve ionization in positive mode. 0.5 \(\mu\)L spiked sample with matrix and NaCl was spotted in duplicate onto a ground steel target plate and dried under vacuum. Mass calibration was conducted using a polysaccharide ladder extracted from beer [47].  
Ionization was carried out using a 337.1 nm laser, and detected in reflectron positive mode with a reflector voltage of 20.02 kV in the mass range of 300 to 2500 \(m\)/z. The final spectra were the sum of 2000 laser shots. MS intensities of four glycans (namely Man 5, Man 6, Man 7, Man 8) released from bovine ribonuclease B (RNase B, Sigma Aldrich, St. Louis, MO, USA) were normalized to the MS intensity of 3-FL internal standard, summed, and considered the normalized relative abundance of glycan.  
Linearity of the MALDI-ToF method was determined by preparing a stock of glycans released from RNase B using free Endo BI-1. Following purification as described above, varying amounts of released \(N\)-glycan was added to a constant amount of 3-FL internal standard and analyzed by MALDI-ToF MS as described above. The summed, normalized intensity was plotted against the volume of \(N\)-glycans added and a linear regression was fit to the data.
###### Abstract  
Semantic segmentation of medical images holds significant potential for enhancing diagnostic and surgical procedures. Radiology specialists can benefit from automated segmentation tools that facilitate identifying and isolating regions of interest in medical scans. Nevertheless, to obtain precise results, sophisticated deep learning models tailored to this specific task must be developed and trained, a capability not universally accessible. Segment Anything Model (SAM) 2 is a foundational model designed for image and video segmentation tasks, built on its predecessor, SAM. This paper introduces a novel approach leveraging SAM 2's video segmentation capabilities to reduce the prompts required to segment an entire volume of medical images. The study first compares SAM and SAM 2's performance in medical image segmentation. Evaluation metrics such as the Jaccard index and Dice score are used to measure precision and segmentation quality. Then, our novel approach is introduced. Statistical tests include comparing precision gains and computational efficiency, focusing on the trade-off between resource use and segmentation time. The results show that SAM 2 achieves an average improvement of 1.76% in the Jaccard index and 1.49% in the Dice score compared to SAM, albeit with a ten-fold increase in segmentation time. Our novel approach to segmentation reduces the number of prompts needed to segment a volume of medical images by 99.95%. We demonstrate that it is possible to segment all the slices of a volume and, even more, of a whole dataset, with a single prompt, achieving results comparable to those obtained by state-of-the-art models explicitly trained for this task. Our approach simplifies the segmentation process, allowing specialists to devote more time to other tasks. The hardware and personnel requirements to obtain these results are much lower than those needed to train a deep learning model from scratch or to modify the behavior of an existing one using model modification techniques.  
Keywords:image segmentation; deep learning; zero-shot learning; medical imaging; foundation models +
Footnote †: journal: _Algorithms_
## 2 Materials and Methods
### Airborne Campaigns  
A thermal camera (FLIR SC655, FLIR Systems, Wilsonville, OR, USA) and a micro-hyperspectral imager (Micro-Hyperspec VNIR model, Headwall Photonics, Fitchburg, MA, USA) were installed on a Cessna aircraft operated by the _Laboratory for Research Methods in Quantitative Remote Sensing_ (IAS, CSIC, Spain). The thermal camera has a 640 \(\times\) 480 pixel resolution with a 24.5 mm f1.0 lens, with radiometric performance assessed in the laboratory using a black body (model P80P, Land Instruments, Dronfield, UK) and through vicarious calibrations using surface temperature measurements (Figure 1).  
The hyperspectral sensor used in this experiment is the micro-hyperspectral instrument operating in the spectral mode of 260 bands at 1.85 nm/pixel and 12-bit radiometric resolution, yielding 6.4 nm FWHM with a 25-micron slit in the 400-885 nm region (Figures 2 and 3). No spectral binning was performed during the acquisition of the imagery. The frame storage rate on board the aircraft was set to 50 fps with an integration time of 18 ms. The 8-mm focal length lens yielded an IFOV of 0.93 mrad and an angular FOV of 50 \(\lx@math@degree\). The hyperspectral sensor was radiometrically calibrated in the laboratory  
Figure 1: (**a**) Thermal imagery acquired from the study area; (**b**) Detail of a single plot.  
using coefficients derived from a calibrated uniform light source and an integrating sphere (CSTMUSS2000C Uniform Source System, LabSphere, North Sutton, NH, USA) using four levels of illumination and six integration times. Ortho-rectification of the hyperspectral imagery was performed using PARGE (ReSe Applications Schl\(\ddot{\text{u}}\)pfer, Wil, Switzerland). Inputs were obtained from an inertial measuring unit (IMU) (MTiG model, Xsens, Enschede, The Netherlands), installed on board, and synchronized with the micro-hyperspectral imager (as described by [17]).  
Two flights were carried out at noon on the solar plane on DOY 76 and DOY 126, 2014 over the experimental field. Flight altitude was 345 m. It delivered a ground resolution of 30 and 20 cm for the thermal and the hyperspectral imagery, respectively.  
Figure 3: Airborne hyperspectral reflectance (**a**) and radiance (**b**) extracted from pure vegetation pixels obtained on DOY 76.  
Figure 2: **(a)** Hyperspectral imagery acquired from the study area; (**b**) Detail of a single plot.
## 4 Capability Evaluation of a Satellite Communication System  
Capability refers to the function, performance, and efficiency that a system must possess to successfully accomplish a specific mission. In this section, an evaluation model based on evidence theory is established to analyze whether the current capability of the SCS aligns with the requirements of the mission task.  
By decomposing the mission task and conducting system analysis, we identify the specific demands associated with the mission. Subsequently, we construct a comprehensivecapability indicator system that encompasses these demands. To measure the satisfaction level of the capability demands, we employ the PHFET to fuse data from different indicators.
## 2 Materials and Methods  
### Numerical Modeling of Humerus GT Fracture with Plate Fixation under Mechanical Testing Loading  
Initially, a three-dimensional solid model of the intact humerus used in the present study was originally developed by a commercial company (Zygote Media Group, Inc., American Fork, UT, USA). This commercially available Zygote solid skeleton model was modified using SolidWorks 2019 (SolidWorks Corporation, Concord, MA, USA) and trimmed to separate the cortical and cancellous bones using Geomagic Freeform (3D Systems, Rock Hill, SC, USA). In this study, the GT fracture model of the humerus was based on a split-type fracture proposed by [PERSON] et al. [2]. Thus, an osteotomy was performed such that the GT bone was completely separated. The PHILOS proximal humer  
Figure 1: Definition of shoulder joint rehabilitation activities.  
internal locking plate from DePuy Synthes (Zuchwil, Switzerland) was modeled in SolidWorks 2019, and implanted using locking screws in the aforementioned GT fracture model according to the clinician's guidance. The humerus bone plate construct was imported into a commercial finite element analysis software ANSYS Workbench 19.2 (ANSYS Inc., Canonsburg, PA, USA) for computational analysis (Figure 2). The materials of cortical bone, cancellous bone, and titanium plate and screws were assumed to be homogeneous, isotropic, and linearly elastic. The elastic moduli of 12, 0.1, and 110 GPa were assigned to the cortical bone, cancellous bone, and implants, respectively. A Poisson's ratio of 0.3 was also applied to all the materials [15]. The bone and implants were free meshed with higher order 10 node tetrahedral elements due to the complex geometry, and a convergence study was performed to confirm the accuracy of the numerical models. Frictionless contact was assigned to the interfaces between the humerus and GT fractured fragment, and between the locking plate and humerus. The bonding constraint was specified to the interfaces between the screws and surrounding bone, and between the plate and screw heads to simulate tightened locking. The distal humerus was fixed in all degrees of freedom and a quantitative displacement of 5 mm was applied to the proximal lumeral head to observe the reaction force. The stiffness of the model was calculated from the observed reaction force and displacement for the humerus positions, defined relative to the vertical plane of 15, 50, and 90\({}^{\circ}\) for abduction and flexion motions and 35, 60, and 90\({}^{\circ}\) for horizontal flexion motion [26]. All flexion motions are same as for the horizontal flexion at 90\({}^{\circ}\).
# Review on Structural Health Evaluation with Acoustic Emission
## 5 Sensing and Signal Processing
Yet, their calibration methods lagged behind till recently. Despite high hopes held earlier, optical fiber-based detectors have not reached a breaking point, mainly from high component cost and from the lack of revolutionary concepts. Still, FBG sensors are advancing as discussed. Several new approaches of signal processing appeared in the last ten years as noted. AE tomography, developed originally in 1992, has made a significant progress of late.
# The Variation of Surface Shape in the Gas Jet Forming
## 7 Prediction Modelling of Surface Shape Parameters  
The sizeable computational error at the interface of the gas-liquid phase in the numerical simulation is hard to correct, making it difficult to accurately predict the surface shape of the gas-jet-formed surface. Based on this issue, we accurately predicted the formed surface shape by establishing a dimensionless prediction model of the gas jet parameters and the surface shape parameters. The model was built by matching equations based on the variation trend of the experimental data and fitting the equation coefficients using the BFGS method. The prediction model for the formed surface diameter is shown in Equation (25).  
\[d_{re}=\beta_{1}+\beta_{2}/{V_{0}}^{2}+\beta_{3}\text{Ln}h, \tag{25}\]  
where \(V_{0}\) is the gas flow rate at the gas nozzle (m/s), \(h\) is the nozzle height (mm), and \(d_{re}\) is the surface diameter of the mirror blank (mm).  
The BFGS method was used to fit the equation coefficients, and the results are shown in Table 5.  
The gas jet parameters were set to \(V_{0}=9.8\) m/s, \(V_{0}=10.3\) m/s, \(h=22\) mm, and \(h=24.5\) mm for the orthogonal experiments, and the following conclusions can be obtained by comparing the experimental data with the calculation results of the surface diameter prediction model established in this paper: The maximum deviation of the experimental results was 0.8554 mm, and the average deviation was 0.3192 mm. The prediction accuracy of the model was much better than that of the numerical simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\beta_{1}\) & \(\beta_{2}\) & \(\beta_{3}\) \\ \hline Density/kg\(\cdot\)m\({}^{-3}\) & 1.225 & 977 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coefficients of the prediction model for the formed surface diameter.  
The same method was used to build a predictive model for the principal curvature, which takes the form shown in Equation (26).  
\[k=\alpha_{1}+\alpha_{2}v+\alpha_{3}{V_{0}}^{2}+\alpha_{4}h^{\alpha 5}, \tag{26}\]  
where \(V_{0}\) is the initial rate at the gas nozzle (m/s), \(h\) is the nozzle height (m), and \(k\) is the principal curvature of the mirror blank (mm\({}^{-1}\)). The BFGS method was used to fit the equation coefficients, and the results are shown in Table 6.  
The gas jet parameters were set to \(V_{0}\) = 9.8 m/s, \(V_{0}\) = 10.3 m/s, \(h\) = 22 mm, and \(h\) = 24.5 mm for the orthogonal experiments, and the following conclusions can be obtained by comparing the experimental data with the calculation results of the principal curvature prediction model established in this paper: The maximum deviation of the experimental results was 0.00735 mm\({}^{-1}\), and the average deviation was 0.00269 mm\({}^{-1}\). The prediction accuracy of the model was much better than that of the numerical simulation.
# Measurement of Ultra-High Speed by Optical Multistage Cascade Frequency Reduction Technology
## 4 Conclusions  
Through two-stage optical cascade frequency reduction, speed segmented \"relay\" measurement is formed between adjacent stages, and the speed measurement bandwidth has been expanded. If we cascade multi-stage frequency reduction optical paths according to this rule, each multi-stage optical path will increase the speed measurement bandwidth of the system, so the multi-stage cascade frequency reduction technology can theoretically expand the upper limit of speed measurement infinitely.  
With multi-stage cascade frequency reduction, the limited-bandwidth digital oscilloscope can be used to achieve the purpose of expanding the bandwidth of multi-time velocity measurement and finally realize ultra-high-velocity profile measurement. The research results of this paper can provide laser interference ultra-high velocity measurement technology with a maximum velocity measurement upper limit of 22 km/s, time resolution of 200 ps, and velocity measurement accuracy of 1%. In the research fields of aerospace industry and natural science, the velocity of objects above 10 km/s or even 20 km/s can be continuously measured in a non-contact way.  
Each use of N-cascade downscaling technology can reduce the original signal frequency to 1/N of the original signal. For ultra-high-speed signal measurements, the bandwidth requirement of the oscilloscope is reduced by a factor of N, which greatly reduces the cost in engineering applications.  
Conceptualization, J.W. ([PERSON]); methodology, T.T.; software, Y.C.; and C.L. ([PERSON]); validation, D.L.; formal analysis, X.J.; investigation, L.T.; resources, S.L.; data curation, W.G.; writing--original draft preparation, L.C.; writing--review and editing, H.M.; visualization, J.W. ([PERSON]); supervision, X.W.; project administration, H.L.; funding acquisition, C.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 62101518, 52105195, and U2241276, and the Foundation of National Key Laboratory of Shock Wave and Detonation Physics, grant number JCKYS2022212001. This work is supported by the China Academy of Engineering Physics.  
The authors declare no conflict of interest.  
Figure 11: Velocity profiles obtained from experiments.
## 3 Results and Discussion
### Various Scan Path
#### 3.3.3 Specific Resistance and Applications  
The previous experiments fabricated a conductive structure using the elliptical scan path in which the most stable deposition was performed, and the specific resistance was measured. The length of the major and minor axes was set equally to 50 \(\upmu\)m, and the laser mode was set to CW. For calculating the specific resistance of the fabricated conductive structure, current-voltage curve was measured using potentiostat/galvanostat. The length of the measured conductive structure was 1.0 \(\times\) 10\({}^{-2}\) m, and the resistance of the conductive structure was calculated as 17.762 \(\Omega\) from Figure 10a. Based on this value, the specific resistance was found to be 26.1 \(\upmu\Omega\)-cm. As shown in Table 3, compared to bulk copper or common electroless plating, the specific resistance was more than 10 times higher. Still, compared to previous LCLD studies, it showed relatively superior performance [26]. In addition, the specific resistance in the LCLD is closely related to the process speed [27]. Therefore, the proposed process was more than 10 times faster than the previous LCLD process and at the same time had a low level of specific resistance, so it could be considered that the economic feasibility and quality had improved significantly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Cu Nanopar-**} \\  & **Bulk Copper** & **Common Electrodes** & **Cuticles after** & **Previous LCLD** & **Proposed Fast LCLD** \\  & **Plating** & **Sintering** & **Process** & **Process** \\ \hline Specific resistance & 1.68 & 2.00 & 4.37 & 10–300 & 26.1 \\ \([\upmu\Omega\)-cm] & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The specific resistance of the fabricated pattern.  
Figure 10: Conductivity test of deposited structure: (**a**) current-voltage curve; (**b**) profile graph of the deposited and the bare surface; (**c**) simple application to LED circuit.
## 6 Evaluation of Compaction Resistance Considering Slip Sinkage
This is because as \(i\) increases, the function in the integral sign of Equation (13) increases. In particular, at \(i\) = 0.2, a common basis to compare the performances of off-road tracked vehicles [2], \(R_{c}\)/\(R_{c0}\) converges to \(3.78n+1\). Accordingly, under the analytical conditions applied in this study, overall compaction resistance \(R_{c}\) was 7.35 (\(n\) = 0.5) to 54.01 (\(n\) = 2.0) times greater than compaction resistance \(R_{c0}\) due to static sinkage. These results indicate that the effect of slip sinkage should be considered when assessing the tractive performance of off-road tracked vehicles and that tractive performance can be overestimated when compaction resistance is calculated considering only static sinkage. When excessive slip sinkage is expected with a large \(i\), it is necessary to apply a proper method to suppress the effect of slip sinkage such as unconventional track suspension systems [35; 36; 37; 38]. The larger \(n\) and \(l\), the larger \(R_{c}\)/\(R_{c0}\). Moreover, the extent of compaction resistance \(R_{c0}\) induced by static sinkage decreases as \(n\) and \(l\) increase. In this case, an increase in \(R_{c}\)/\(R_{c0}\) does not translate into an increase of the absolute extent of overall compaction resistance, but this result indicates that the effect of slip sinkage on compaction resistance increases. That is, even as the soil becomes dense and the soil-track contact length extends (i.e., as \(n\) and \(l\) increase), thereby reducing off-road tracked vehicle static sinkage (\(z_{0}\)) and \(R_{c0}\), compaction resistance induced by slip sinkage (\(z_{j}\)) can still greatly affect tractive performance. Considering that slip sinkage is caused by ground shearing, not only ground sinkage characteristics but also shear characteristics should be included when calculating the compaction resistance of off-road tracked vehicles. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range** \\ \hline \(n\) & 0.5, 1.0, 1.5, 2.0 \\ \(l\) (mm) & 2000, 3000, 4000, 5000 \\ \(i\) & 0–0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter ranges considered in this study. Figure 14: Normalized compaction resistance considering slip sinkage \(R_{c}\)/\(R_{c0}\): (**a**) \(n=0.5\), (**b**) \(n=1.0\), (**c**) \(n=1.5\), and (**d**) \(n=2.0\).