esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Sea Underwater Cooperative Operation of
## 2 Related Studies on Cooperative Operations
### Manned Submersibles and Other Underwater Equipment  
In April 2018, the \"Ocean No. 6\" scientific research ship of the China Geological Survey transported the Hai Ma ROV and the \"Exploration No. 1\" scientific research ship of the Institute of Deep-Sea Science and Engineering of the Chinese Academy of Sciences transported the Shenhai Yongshi HOV. They conducted a joint dive in the \"Haima Cold Spring\" area in the western part of the Pearl River Mouth Basin of the South China Sea. The HOV and ROV cooperatively photographed and recorded each other and exchanged national flags at a predetermined position on the seabed of \"Haima Cold Spring\" at a depth of 1410 m (Figure 2). This was the first time China had conducted joint scientific research using two types of domestically made deep-sea submersibles with independent intellectual property rights, thereby, giving full play to the respective functional advantages of both submersibles. This was a pioneer demonstration of cooperative operation of domestic deep-sea equipment [25].  
In September 2020, the deep-sea mining joint experimental team from China Mechants Industry Group, Guangzhou Marine Geological Survey, the Institute of Deep-Sea  
Figure 1: Cooperative operation of Jiaolong HOV and Longzhu ARV. (**a**) Longzhu ARV is placed on the sampling basket of the Jiaolong HOV before diving. (**b**) Longzhu ARV dived to the seabed with Jiaolong HOV. The Chinese characters on ARV is Longzhu, which is the name of ARV.  
Figure 2: Haima ROV and Shenhai Yongshi HOV handover national flags.  
Science and Engineering of the Chinese Academy of Sciences, and Zhongtian Science and Technology Submarine Cable Co., Ltd. completed the first cooperative operation of a deep-sea mining vehicle with the Shenhai Yongshi HOV and the \"Exploration No. 2\" scientific research ship. This realized the continuous in-situ real-time monitoring and sampling in the mining test area, in which the environment was sampled and investigated before and after the test. This provided basic data support to effectively reduce the impact of deep-sea mining on the natural environment [26; 27].  
In November 2020, the Fendouzhe HOV and the deep-sea video lander Canghai was led by the China Central Radio and Television Station and was jointly developed by several institutions, conducted a cooperative operation in the Mariana Trench (Figure 3). The Fendouzhe HOV accurately determined the position of the Canghai Lander using underwater positioning technology and actively approached it after sitting on the seabed. Both systems used underwater blue-green lasers to communicate with each other, making this the first time in history that a broadcast video was broadcast from a cabin on a manned submersible 10,000 m below on the ocean floor. This broke through a series of common problems in the marine research fields, such as deep-sea video acquisition, data processing, and signal transmission, and provided strong technical support for deep-sea environmental exploration and scientific research [28].
# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
## 4 Experimental Results and Analysis
### Training Details  
The ASFusion network proposed in this study was trained on the modulated Road-Scene dataset, with 40% of the images used for validation. The training data were shuffledand divided into 18 batches. For each batch, training images were randomly selected from either the IR or visible light sources, and the images were randomly cropped into 64 \(\times\) 64 patches and normalized to values between 0 and 1. The AFFusion parameters were updated using the AWA strategy, which performed weight allocation operations. The learning rate was initialized as \(1\times 10^{-4}\) and then exponentially decayed. Based on the ablation experiment, hyperparameters controlling the weights of each sub-loss term were set to \(\lambda_{1}=1.5\) and \(\lambda_{2}=1\), and the training process was iterated three times. After each batch of training, the performance of AFFusion was evaluated using the validation set. Throughout the training process, the AWA strategy utilized the VGG16 to extract features from the IR and visible light images at different network layers. After calculating the corresponding feature values and normalizing them, the strategy generated corresponding weight values \(\omega_{ir}\) and \(\omega_{vis}\) for IR and visible light images, respectively. These weight values play an important regulatory role in constructing each loss function and can maximize the preservation of salient texture details in both the IR and visible light images in the fused image.
# Dynamic Torsional Stiffness of Reducers and Its Testing Method
## 2 Dynamic Characteristics of Torsional Stiffness
### Effect of the Loading Rate Dependence on Hysteresis Curve Method  
The analytical expression of Equation (4) can be given as Equation (6).  
\[\theta_{out}(\tau,\dot{\tau})=\frac{\int_{0}^{t}\dot{\tau}(t)dt}{K_{n}}+\frac{dE _{f}(t)}{\mathrm{d}\tau(t)}+\theta_{g} \tag{6}\]  
where \(K_{n}\) is the nominal torsional stiffness of the reducer, \(t\) is the loading time, \(\dot{\tau}(t)\) is a function of the loading rate, \(\tau(t)\) represents the torque function, \(E_{f}(t)\) is the dissipated energy caused by internal and external friction of the reducer, and \(\theta_{g}(\tau)\) can be considered as a constant \(\theta_{g}\) during testing.  
During testing, the reducer is typically subjected to the target torque \(\tau_{r}\). Let us assume the loading rates as \(\dot{\tau}_{1}\) and \(\dot{\tau}_{2}\), where \(\dot{\tau}_{1}>\dot{\tau}_{2}\); thus, \(d\tau_{1}(t)>d\tau_{2}(t)\). Assuming \(dE_{t}(t)\) remains constant, the test results are illustrated in Equations (7) and (8).  
\[\theta_{out1}(\tau_{r},\dot{\tau}_{1})=\frac{\tau_{r}}{K_{n}}+\frac{\mathrm{d} E_{f}(t)}{d\tau_{1}(t)}+\theta_{g} \tag{7}\]  
\[\theta_{out2}(\tau_{r},\dot{\tau}_{2})=\frac{\tau_{r}}{K_{n}}+\frac{\mathrm{d} E_{f}(t)}{d\tau_{2}(t)}+\theta_{g} \tag{8}\]  
At this point, \(\theta_{out1}(\tau_{r},\dot{\tau}_{1})\
eq\theta_{out2}(\tau,\dot{\tau}_{2})\) calculates the torsional stiffness \(K_{s1}\) and \(K_{s2}\) of the reducer based on this result, as shown in Equations (9) and (10).  
\[K_{s1}=\frac{\tau_{r}}{\theta_{out1}(\tau_{r},\dot{\tau}_{1})} \tag{9}\]  
\[K_{s2}=\frac{\tau_{r}}{\theta_{out2}(\tau_{r},\dot{\tau}_{2})} \tag{10}\]  
The analysis reveals that \(K_{s1}\
eq K_{s2}\) is a consequence of the loading rate dependence on the hysteresis curve method. The impact on the hysteresis curve method diminishes as the loading rate decreases. However, it is important to note that a loading rate of 0 is merely an ideal scenario and is not achievable in reality. The influence of loading rate dependence persists even under low loading rates.
## 3 Quantitative Analysis of No Splash
### Bubble Feature Points  
Figure 6 shows the displacement of bubble feature points of no splash of different \(\lambda\) over time in the no splash case. The red dash represents the displacement of feature points in the X axial direction; the blue double dotted line represents the displacement of feature points in the \(Y\) axial direction; the orange long line represents the displacement of feature points in the \(45^{\circ}\) axial direction; and the green dotted line represents the displacement of feature points in the \(-45^{\circ}\) axial direction. Subfigure (a) shows the displacement of bubble feature point (\(\lambda\) = 0.463). During the first collapse, the displacement of the feature points from the maximum to the end of the first collapse was nearly 0.6 mm. The displacement in the \(45^{\circ}\) axial directions and X axis was closer to the displacement in the \(-45^{\circ}\) axial direction. However, the displacement in the \(Y\) axial direction was slightly smaller than the displacements in the other three directions. Subfigure (b) (\(\lambda\) = 0.506) shows a similar trend to the subfigure (a).  
However, subfigure (c) shows a different trend (\(\lambda\) = 0.581). Under this experimental condition, the trend of bubble collapse is obvious. Meanwhile, the variations of bubble wall shrinkage become significant.  
Figure 5: The process of bubble collapse within droplet with composite splash.  
Figure 7 shows the distance between the two ends of the bubble X axial corresponding to the three \(\lambda\) in the no splash case over time. It can be seen that the distance in the X axial direction of the bubble gradually decreases with time, which is the process of the bubble wall gradually shrinking. The larger \(\lambda\), the greater the difference in initial displacement between the two ends of the X axial, and the longer the collapse time. In the no splash cases, the bubble collapse process has a significant lag when the \(\lambda\) is large.
# Multifunction RF Systems for Naval Platforms
## 4 MFRF System Prototype Development Programs
### InTop
#### 4.2.4 Submarine Wideband SATCOM Antenna Subsystem  
The submarine SATCOM antenna subsystem employed separate Tx and Rx arrays to supply SATCOM services from C-band through to V-band. It supported from four to at least eight simultaneous communication links.
# Integrated Framework for Bus Timetabling and Scheduling in Multi-Line Operation Mode
## 4 Case Study and Discussion
### Results and Discussion
#### 4.2.2 Contrast Experiment of Generation Algorithm  
A feasible solution generation algorithm CEG is proposed in Section 3.2. CEG requires vehicle scheduling compliance with the FIFO rules and timetabling compliance with UI rules. To verify the coverage of feasible solutions' objective function value and the efficiency of generating feasible solutions, we compare CEG with commonly used algorithms: random algorithm (CRD) and partly greedy algorithm (CERD). CRD is completely random, scheduling vehicles randomly based on the random frequency in each period. CERD is partly random, scheduling vehicles randomly but timetabling in compliance with UI rules. Basic logical rules are considered in these algorithms.  
Figure 16: The Gantt diagram from of Solution-A.  
Figure 15: Diagram of demand and supply for each line.  
We changed the total number of solutions \(G_{max}\), and did six comparative experiments. The feasible solution generation efficiencies by different algorithms are shown in Table 4. The number of feasible solutions is recorded as \(N_{es}\) and the proportion of feasible solutions is recorded as \(P_{es}\). It can be seen from the results that the efficiency of CRD is the lowest, at less than 4%. CERD shows a big improvement in efficiency, of above 50%. CEG has the highest efficiency of 65%, which is 15% higher than CERD. We also compare the range of the objective function values by different algorithms. The results are shown in Table 5, where \(ROC\) represents the range of operating costs and \(RWT\) represents the range of passenger waiting time. Figure 17 shows the result of objective function values when \(G_{max}=1000\). Obviously, the \(ROC\) and \(RWT\) of CEG is the broadest. CEG and CERD have a greater improvement in \(ROC\) and \(RWT\) compared with CRD. It should be mentioned that CEG and CERD can find more low-passenger-waiting-time solutions, which is meaningful for the operation enterprises pursuing service quality. In general, CEG not only has higher \(P_{es}\), but also has broader \(ROC\) and \(RWT\).
# Evaluation of Low-Temperature Performance of Recycled
## 1 Introduction
According to whether chemical reactions occur during the aging process, asphalt mixture aging can be divided into the two types of: thermally reversible aging and irreversible chemical aging ([PERSON] et al., 2018). Numerous studies have shown that solvent recovery or heating during the regeneration process will destroy the reversible structural molecules in asphalt, so the impact of reversible aging on the low-temperature properties of the asphalt mixture can be ignored ([PERSON] et al., 2018). However, irreversible aging cannot be eliminated and can be divided into thermal- oxidative aging, ultraviolet aging, exudation, and biodegradation. Among these, high-temperature oxidation and ultraviolet light are the primary reasons for the aging of pavement materials. The varying degrees of RAP undergoing the aforementioned aging processes will result in varying thermal histories, potentially affecting the LTP of RAMs. Consequently, in this project, the indoor production of thermal- oxygen aging reclaimed asphalt pavement (THRAP) and ultraviolet aging reclaimed asphalt pavement (UVRAP) is employed to simulate the different thermal histories of the RAPs. In this study, the thermal stress restrained specimen test (TSRST), the trabecular bending test, and the bending beam creep test (BBCT) are utilized to evaluate RAM of the same grade with varying contents of RAPs with different thermal histories. Scanning electron microscopy (SEM) was employed to investigate the micromorphology of the interface between RAP and asphalt, as well as new aggregates and asphalt in the RAM. The analysis focused on the impact of the RAP content and thermal histories on the LTP of RAMs from both phenomenological and microscopic perspectives. The findings of this research contribute to the understanding of the feasibility of using RAM with RAPs from different sources in the northern region, ultimately leading to a reduction in the demand for new materials, a decrease in environmental pollution, and positive social, environmental, and economic outcomes. The steps of the research process are illustrated in Figure 1.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
###### Abstract  
As urban traffic complexity increases, realistic multimodal simulation environments are essential for evaluating transportation safety and human behavior. This study introduces a novel multimodal, multi-participant co-simulation framework designed to comprehensively model interactions between drivers, bicyclists, and pedestrians. The framework integrates CARLA, a high-fidelity driving simulator, with PTV Vissim, a widely used microscopic traffic simulation tool. This integration was achieved through the development of custom scripts in Python and C++ that enable real-time data exchange and synchronization between the platforms. Additionally, physiological sensors, including heart rate monitors, electrodermal activity sensors, and EEG devices, were integrated using Lab Streaming Layer to capture physiological responses under different traffic conditions. Three experimental case studies validate the system's capabilities. In the first, cyclists showed a significant rightward lane shift (from 0.94 m to 1.14 m, \(p<0.00001\)) and elevated heart rates (69.45 to 72.75 bpm, \(p<0.00001\)) in response to overtaking vehicles. In the second, pedestrians exhibited more conservative gap acceptance behavior at 50 mph vs. 30 mph (gap acceptance time: 3.70 vs. 3.18 s, \(p<0.00001\)), with corresponding increases in HR (3.54 bpm vs. 1.91 bpm post-event). In the third case study, mean vehicle speeds recorded during simulated driving were compared with real-world field data along urban corridors, demonstrating strong alignment and validating the system's ability to reproduce realistic traffic conditions. These findings demonstrate the system's effectiveness in capturing dynamic, real-time human responses and provide a foundation for advancing human-centered, multimodal traffic research.  
driving simulator; bicycle simulator; pedestrian simulator; co-simulation; multimodal transportation +
Footnote †: journal: Applied sciences
###### Abstract  
In this research, a key optical component for multi-degree-of-freedom (MDOF) surface encoder was designed, fabricated and evaluated. In a MDOF grating interferometry system, there are four diffraction beams from a two-axis scale grating and reference grating, respectively. For further modulation, these beams will propagate more than 100 mm, which makes paralleling these beams necessary. In previous research, collimation lens, separate prisms and a home fabricated diffraction device by combining four separate one-axis line gratings in a glass substrate have been demonstrated. However, large power loss and assembly complicity makes these techniques less competitive. For solving this problem, this research proposed a new lens module, which is an improved type prism, quadrangular frustum pyramid (QFP) prism. The prism is designed in such a way that these four reflected beams from the grating are symmetrically incident into the prism through the upper surface, total reflected on the inner sides of the prism, and then parallel getting through the bottom surface. A prism that allows an incident beam diameter of 1 mm and four paralleling beams with a 10 mm distance between the two diffraction beams along one direction was designed, fabricated and tested. Testing results based on an entire grating interferometry system verified that the proposal in this research is greatly effective in beam paralleling in terms of less power loss and high paralleling and greatly reduces the assembly complicity, which will eventually be beneficial for grating interferometry application.  
surface encoder; multi-degree-of-freedom; interferometry; grating; prism +
Footnote †: journal: Applied Optics and Photonics
## 4 Conclusions  
The presented study attempted to apply an SVM-supervised classifier to classify and predict the compressive strength (UCS) behavior of Maragheh limestone. In this regard, a comprehensive field survey and sampling were performed in the Maragheh region, leading to a total of 120 specimens of limestone being obtained from different sites in the studied region. The samples were isolated and transferred into a geotechnical laboratory to conduct UCS tests as per ASTM regulations in a UCT device. After performing the laboratory and modeling procedures, the following results were obtained:  
* An extensive database was provided using the UCS experimental records to provide a detailed analysis of the SVM model. The prepared database was randomly divided into a training test (80% of the primary database) and a testing set (the remaining 20% of the primary database). The SVM model was trained on the training set and tested on the testing dataset.
* The machine learning technique was controlled by a confusion matrix, loss function, and statistical error analysis for both the testing and training datasets, which provided relevant information about the capability and performance of the model.
* According to the SVM results, the model's accuracy was 0.91, and its precision was 0.86. In addition, the estimated loss function was reduced to 0.2, which is considered to be a good capability for learning. In addition, the other evaluating criteria, such as recall, were estimated at about 0.78, which is considerable for the learning rate of the SVM model.
* By normalizing the experiment data with the Q-Q chart, it appeared that the data chart followed a normal distribution law within the confidence interval of 93.7%.
* The correlation results for both the testing and training data with the actual results from the experiments indicated that the model was trained remarkably well (R\({}^{2}\) = 0.967) and was tested properly (R\({}^{2}\) = 0.955).  
The statistical error rates obtained for the SVM model indicated that the model operated with a low error rate, including MAE = 0.237463, MSE = 0.210540, and RMSE = 0.200674 for the training set and 0.451089, 0.435988, and 0.410456 for the testing set, respectively.  
A.C., L.Z. and S.A.: writing--original draft preparation, validation, methodology, investigation, resources; J.X. and X.Y.: visualization, methodology; M.A. and R.D.: supervision, formal analysis, conceptualization, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors wish to thank the Department of Civil Engineering, University of Tabriz, for providing permission to conduct the geotechnical investigations. In addition, the authors would like to thank the anonymous reviewers for providing invaluable review comments and recommendations for improving the scientific level of the article.
# Innovative Acoustic-Hydraulic Method for High-Performance Fine Liquid Atomization
## 2 Materials and Methods  
We considered the following design of an acoustic-hydraulic nozzle (Figure 1). In direction 1, liquid is supplied to cylindrical tube 2 under pressure \(p\). The ultrasonic emitter 3 with flat oscillating surface oscillates along the axis of the tube. In zone 4, the liquid cavitates under the influence of ultrasonic radiation and flows out of outlet 5 in the form of a spray of drops 6. The outlet is located opposite the end surface of the emitter at a distance of 7.  
The main geometric parameters of the nozzle used in the calculations:  
* The area of the end part of the ultrasonic emitter 3--\(S_{1}\);
* The area of the outlet 5--\(S_{2}\);
* The cross-sectional area of the tube 2--\(S_{3}\);
* The height of the cavitation zone (distance from the surface of emitter to the outlet) 7--L.  
Using a nozzle, we sprayed the settled tap water. Water contains a small amount of impurities (hardness salts). Droplet size was measured using a Spraytec instrument (Malvern Instruments, Malvern, UK). The operating principle of the measuring system was based on laser diffraction. The Spraytec system allowed us to measure particle dispersion with a frequency of up to 10 kHz in the range from 0.1 to 2000 microns with an accuracy better than \(\pm\)1%. Particle size calculations were performed using a proprietary multiple scattering analysis algorithm. An example of a histogram and statistical characteristics of the particle size distribution obtained using the Spraytec system is shown in Figure 2. Statistical data measured using Spraytec system for different pressure values at an intensity of 70 W/cm\({}^{2}\) are shown in the Supplementary Materials (Figures S1-S8).  
The experimental setup scheme is shown in Figure 3a. Figure 3b shows the operation of the experimental setup.  
Figure 1: Acoustic-hydraulic nozzle scheme: 1—liquid supply direction, 2—cylindrical tube, 3—ultrasonic emitter, 4—cavitation zone, 5—outlet, 6—liquid drops, 7—cavitation zone height \(L\).  
Figure 2: Example of a histogram and statistical characteristics of the particle size distribution formed using the developed nozzle (obtained using Spraytec).  
The operating pressure range of the sprayed liquid was 2-9 bar, the radiation intensity was up to 100 W/cm\({}^{2}\) (the maximum amplitude of ultrasonic vibrations was up to 40 \(\upmu\)m).  
To describe the mechanisms of sputtering and the physical processes occurring during this process, we used mathematical modeling methods.