esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of Biogenic Amines  
Biogenic amines were analyzed using the high-performance liquid chromatography (HPLC) method of [PERSON] et al. [34]. The HPLC equipment used was Shimadzu Prominence LC-20 AT series (Japan) HPLC with an autosampler (Ssl20 AC, Shimadzu, Japan), a diode array detector (SPD-M20A, Shimadzu, Japan), and an Inertsil column (GL Sciences, ODS-3, 5 \(\upmu\)m, 4.6 \(\times\) 250 mm). This method originated from EU-suggested methods [14]. To extract biogenic amines, 10 mL of 0.4 M perchloric acid was added to a 5 g sample, and the mixture was homogenized by an ultra-turnax homogenizer (IKA T 25, Digital, Taufkirchen, Germany) in an ice bath and centrifuged (MPW 350 R. MPW Med. Instruments., Warsaw, Poland) at 2440 g at 4 \({}^{\circ}\)C for 10 min. The supernatant was collected, and the residue was extracted again with 10 mL of a 0.4 M perchloric acid solution. Both supernatants were combined and filtered through Whatman paper (No. 42). The final volume was adjusted to 25 mL with 0.4 M perchloric acid. Each sample extract and diluted standard solutions of 0.5 mL were mixed with 100 \(\upmu\)L of 2 N sodium hydroxide and 150 \(\upmu\)L of saturated sodium bicarbonate. One milliliter of a dansyl chloride solution (10 mg/mL) prepared in acetone was added to the mixture. This was mixed well, and incubated at 40 \({}^{\circ}\)C for 45 min and cooled down to room temperature in 10 min. Subsequently, the residual dansyl chloride was removed by the addition of 50 \(\upmu\)L 25% ammonia solution. After 30 min of incubation at room temperature, the extract was adjusted to 5 mL with the ammonium acetate: acetonitrile mixture (1:1 \(v/v\)) and mixed well with a vortex (Nuve NM 110, Ankara, Turkey). The extract was filtered through filters with 0.45 \(\upmu\)m pore sizes (Millipore Co., Bedford, MA, USA) and injected into HPLC. The gradient elution system contained 0.1 M ammonium acetate as solvent A and acetonitrile as solvent B. The gradient elution was initiated with 50% A and 50% B, and terminated in 19 min with 90% B, with a run time of 20 min. The system was equilibrated for 8 min before the next run. The flow rate was 0.9 mL/min, and a 20 \(\upmu\)L sample was injected into the column. The column temperature was 40 \({}^{\circ}\)C, and amines were detected at 254 nm. Triplicate sampling was carried out and performed separately per group at each sampling point.
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
## 3 Methods
### Multi-Robot Collaborative Navigation
#### 3.3.2 Curriculum Design for Reward Shaping  
In the last section, we modified the network structure and proposed the PDDPG to address multi-robot navigation tasks. Besides the good network structure like PDDPG, proper reward formulation is one of the most essential parts of enabling a collaborative capability for a multi-robot navigation system.  
When the robots navigate in a mapless environment, the reward would be very sparse. PDDPG would took a long time to converge to a satisfying policy module. A direct training with PDDPG on the collaborative navigation task with eight robots does not yield acceptable performance. To address this issue, we use the curriculum learning [43] which trains the robot with a sequence of progressively more difficult tasks. It paves the way to train the agent to accomplish the difficult tasks. In our proposed method, we only divide the collaborative navigation task into two stages. First, the group of robots needs to construct the formation. In addition, then the robots would be trained to keep the initial formation during navigation.  
For the formation construction task, we adjust the reward function of the single robot navigation task to the multi-robot version. The approaching reward of each robot would be summed up as a group approaching reward, as shown in Equation (8). When any of the robots collide, the environment will be reset and the episode will be ended. In the view of training a good reinforcement learning agent, terminating the episode at an appropriate step can efficiently accelerate the agent training. After that, we train the robot to accomplish the collaborative navigation task based on the formation construction policy module. Besides the aforementioned reward, we add the formation constraints in the group reward term \(r_{formation}\). This term represents some deformation penalties in our system. Specifically, the algorithm calculates the distances between the robots and save their ratios at the first timestep. When the group of robots are navigating, the algorithm would check the distance ratios and give relative rewards. Moreover, if the speed of one robot is much higher than the mean, the formation keeping constraints would penalize the robot:\[r(s,a)=\begin{cases}r_{arrive}&(\rho<d_{goal})\\ r_{collision}&(\min(d_{1},d_{2},...,d_{180})<d_{collision})\\ \Sigma_{1}^{n}k(\rho_{t-1}^{i}-\rho_{t}^{i})&(At\;each\;time\;step\;t)\\ r_{formation}&(Only\;at\;the\;formation\;keeping\;stage).\end{cases} \tag{8}\]
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 4 Applications of MMP to Biology and Agriculture
### Biological Tissues
It was concluded that it is possible to use fluorescence linear diattenuation and linear polarizance as new diagnostic parameters for detecting precancer. It can be summarized that, although the MM contains rich, microstructural information about tissues, such information is difficult to obtain directly from the MM elements. To solve this problem, several methods have been proposed for transforming the MM elementsinto various groups of derived parameters that have a clearer physical and structural meaning [50; 51; 52; 53; 54]. The capabilities of polarimetric diagnostics can be tested on model tissue-like samples. The MM of soybean oil, which has a high degree of tissue-like properties, was studied [40].
## 5 Simulation Results and Discussion
### Effects of Initial Energy  
In this section, we compare different protocols' performances with the different initial energy changes among 400 and 1200 J in test phase. The speed of nodes is 10 m/s and the packet send rate is 1 Hz. There are 100 nodes moving in the network. The simulation results are shown in Figures 14-16.  
Figure 14 depicts the relationship between the average E2E delay and different initial energy. As we can see from the figure, as the initial energy increases, the average E2E delay of DSEGR is similar to QNGPSR. The delay of DSEGR and QNGPSR is better than the others since they reduce the routing void area.  
Figure 15 illustrates the results of PDR versus different initial energy. With the increase of initial energy, the PDR of all the protocols also increase. This is because the node dies late. It is clear that the DSEGR performs better PDR than the other protocols. The reason for this is that DSEGR can select the next hop considering neighbors' residual energy. Although FEQSAI considers the E2E transmission energy, which cannot converge due to retraining at different maps, the PDR is lowest.  
Figure 14: Performance comparison of the time of the first dead node among GPSR, QNGPSR, DSEGR, and FEQSAI with different initial packet energy.  
Figure 15: Performance comparison of PDR among GPSR, QNGPSR, DSEGR, and FEQSAI with different initial packet energy.  
Figure 16 depicts the effects of the time of the time of the first dead node on different initial energies. As the initial energy increases, the nodes die later and later for all tested protocols. As we can see from the figure, the time of the first dead node for DSEGR is better than the other protocols for different initial energies. There is not even a dead node for larger initial energies, such as 1000 J and 1200 J, because the DSEGR involves an energy prediction method.
## 1 Introduction
### Limitations  
Despite these contributions, our study acknowledges certain limitations. While our findings offer valuable insights, the scope of this research is confined to specific FL configurations and dataset characteristics. Further exploration of different FL architectures, diverse datasets, and real-world deployment challenges remains an area ripe for future investigation. Additionally, the scalability analysis, while comprehensive, focuses on a limited range of users and could benefit from further exploration with more extensive user groups in practical scenarios.
## 4 Numerical Simulation Results and Analysis
### Effect of Vertical Pressure on Temperature Field of Grain Pile  
To reveal the influence of pressure on the temperature field distribution of grain pile, the temperature field of grain pile at the same time under different pressures was simulated. Figure 8 shows the middle plane temperature distribution of the grain pile under different vertical pressures for 72 h. The temperature change trend of the bulk grain pile is essentially the same under different vertical pressures. When the pressure is 50 kPa, the temperature field distribution of the grain pile is uneven, and the isotherm curve is large. With the increase in vertical pressure, the porosity of the bulk grain pile decreases, the contact area between the grains increases, and the convection heat transfer is hindered. When the vertical pressure is 200 kPa, the temperature distribution in the bulk grain pile is uniform,  
Figure 7: Temperature distribution in the middle plane of the grain pile at different time points: (a) 1 h; (b) 24 h; (c) 48 h; (d) 72 h.  
and the isotherm does not exhibit a large bend. Considering the isotherm of 23.75 \({}^{\circ}\)C as an example, the dotted line in Figure 8 is the vertical line where the temperature difference between the top and bottom of the storage structure is the largest. When the vertical pressure is 50 kPa, the temperature difference between the top and bottom of the storage structure is the largest at 2.75 \({}^{\circ}\)C. With increase in vertical pressure, the convection heat transfer is hindered, and the temperature difference decreases. When the vertical pressure is 100 kPa, the temperature difference is 1.57 \({}^{\circ}\)C, and the minimum temperature difference is 0.82 \({}^{\circ}\)C at 200 kPa. When the vertical pressure increases from 50 kPa to 200 kPa, the rate of change of temperature at the top and bottom of the silo decreases from 11.58% to 3.43%. With increase in the vertical pressure, the transfer path is reduced, the transfer speed is accelerated, and the temperature distribution becomes more uniform. Pressure has an obvious effect on the temperature distribution and heat transfer of the grain pile.
# Motion Simulation and Human-Computer Interaction System for Lunar Exploration
## 2 Materials and Methods
### Modeling of Lunar Environment and Rover
#### 2.2.3 Posture Adjustment Module  
The rover's posture adjustment module--that is, the suspension--is a mechanism that connects the wheels and the body of the rover. Its functions include power transmission, buffering, and vibration reduction [18], which affect the performance, stability, ride comfort, and anti-overturning ability of the rover. We designed the posture adjustment module. Through the above work, it was ensured that the astronauts will have a stable and comfortable driving experience when the rover works on the rugged lunar surface.  
According to the connection between the wheels and the body, the suspension was divided into independent and non-independent suspensions. We used four-wheel independent semi-active suspension to ensure comfort and reliability while reducing energy consumption.  
The PID control model of the semi-active posture adjustment system is shown in Figure 7. \(k\) is the spring stiffness, \(c\) is the damping coefficient of the shock absorber, \(f_{i}\) is the driving force of the \(i\) wheel generated by PID adjustment, \(x_{0}\) is the excitation displacement, \(x_{1}\) is the displacement of the suspension connection, and \(x_{2}\) is the body displacement.  
According to the model, the dynamic equation of the suspension can be established [19; 20]:  
\[m\ddot{x_{1}}-k(x_{1}-x_{0})-c\big{(}\dot{x_{1}}-\dot{x_{0}}\big{)}+f_{i}=0 \tag{14}\]  
\[m^{\prime}\ddot{x_{2}}-f_{i}=0 \tag{15}\]  
To realize the leveling of the car body, the car body inclination was regarded as the object of PID control to reduce turbulence. PID control is a commonly used control method in semi-active suspensions and has strong applicability. Its structural framework is shown in Figure 8. In Unity 3D, we used the collider component and wrote PID adjustment scripts to carry out the simulation:  
\[f_{\alpha}=K\bigg{(}k_{p}\alpha+k_{i}\int\alpha dt+k_{d}\frac{d\alpha}{dt} \bigg{)} \tag{16}\]  
\[f_{\beta}=K\bigg{(}k_{p}\beta+k_{i}\int\beta dt+k_{d}\frac{d\beta}{dt}\bigg{)} \tag{17}\]  
Figure 8: Structure diagram of PID control.  
Figure 7: PID control model of semi-active suspension.  
We took the pitch angle \(\alpha\) and roll angle \(\beta\) of the car body as the input of the PID control, and generated the driving force \(f_{i}\) of the \(i\) wheel as the output of the system. The output was \(f_{\alpha}\) and \(f_{\beta}\), which obtained \(f_{i}\) after different linear combinations. The structure diagram of the PID control is shown in Figure 8.
## 6 Limitations and Future Work  
There are two limitations to our work. Related future work is also proposed. Firstly, the purpose of this paper is to demonstrate the efficiency of the proposed semi-supervised approach in automatic modulation classification. Our work has only been validated on one dataset. In practical applications, the data size and class number are not fixed. Therefore, the generalization ability of our proposed method needs further verification. Secondly, our work only classifies known modulation schemes. In practical applications, collected unlabeled signal samples will inevitably include unknown modulation schemes. In future work, we will consider automatic modulation classification in open-set scenarios.
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 5 The Improved More Efficient Protocol
### System Initialization
#### 5.1.1 OBU Registration Protocol  
When an OBU register to the system with its identity \(ID_{i}\), TA does the following:  
1. Check the validity of the identity \(ID_{i}\). If not valid, terminate the protocol;
2. Choose a fixed-length random number \(rnd\in Z_{p}^{*}\), compute the pseudo-id \(PID_{i}=Enc_{u}(rnd||ID_{i}||h(rnd||ID_{i}))\);
3. Set \(S_{i}=g_{1}^{\frac{n(PID_{i})+u}{n}}\in G_{1}\).
4. Return to OBU the private key \(sk_{i}=(PID_{i},S_{i})\).
Considering real application, in which retinal images are captured by different fundus cameras under different environments, we applied two different preprocessing to enhance the input image. The majority voting scheme is applied to obtain the final DME grade with a high confidence rate, obtaining a performance accuracy of 91.12%, sensitivity of 91.12%, and specificity of 93.00%. While Optical Coherent Tomography (OCT) has become a reliable method for efficiently detecting DME, we opted to utilize retinal images as input data due to the high cost of OCT equipment. In many developing countries, this equipment is only accessible in major hospitals or specialized eye clinics located in urban areas. Given the potential \begin{table}
\begin{tabular}{c c c} \hline \hline
**Grade** & **Severity** & **Details** \\ \hline
0 & Healthy & No visible HaExs near the macula \\ \hline
1 & NCSME &
\begin{tabular}{c} Distance between macula and HaExs \textgreater{} \\ one papilla diameter \\ \end{tabular} \\ \hline
2 & CSME &
\begin{tabular}{c} Distance between the macula and HaExs \\ \textless{}= one papilla diameter \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: DME grading criteria provided by [5]. application of our proposed system in remote areas or within telemedicine frameworks, retinal images serve as more suitable input data. The rest of the paper is organized as follows. In Section 2, we briefly describe some related works previously reported in the literature and a detailed description of the proposed system is provided in Section 3. Experimental results are shown in Section 4, and in Section 5, we provide a discussion about some limitations and challenges of the proposed algorithm. Finally, the conclusions and future works derived from this paper are provided in Section 6. Additionally, mathematical developments of some concepts are provided in the Appendix A.
## 4 Analysis of Sound Radiation Characteristics for Reinforced Laminated Cylindrical Shell Structures
### Effect of Reinforcement Method
#### 4.2.1 Effects of Longitudinal Reinforcement  
The sound power level of the cylindrical shell without any reinforcement and with 4 and 12 longitudinal reinforcements is calculated, and the results are shown in Figure 12.  
Figure 12: Structural sound power level curves under longitudinal reinforcement. (**a**) 4 longitudinal reinforcements; (**b**) 12 longitudinal reinforcements.  
The total radiated sound power levels of 4 and 12 longitudinally reinforced cylindrical shells and the unreinforced cylindrical shell are 83.63 dB, 85.05 dB, and 96.21 dB, respectively. From the perspective of the overall sound power level, the sound radiation performance of the unreinforced cylindrical shell is the best. It can be observed from the graph that, at low frequencies, the addition of longitudinal reinforcement causes an increase in the structural sound radiation peaks. This is because the addition of longitudinal reinforcements increases the circumferential strength of the cylindrical shell, making it difficult for the cylindrical shell to undergo bending deformation, resulting in lower sound radiation power levels compared to the unreinforced cylindrical shell. As the frequency increases, the axial vibration of the cylindrical shell becomes more intense, and at the same time, the restraining effect of longitudinal reinforcement weakens, resulting in more vibration and higher sound radiation peaks. This is unfavorable for reducing the structural sound radiation performance of the cylindrical shell.
## 2 Materials and Methods
### Framework Training and Evaluation  
The dataset (380 images) was partitioned into distinct subsets to facilitate effective training and unbiased evaluation. Specifically, 80% of the data was allocated for training and the remaining data were split evenly between testing and validation, each receiving 10% [29]. This distribution ensured that the model was tested on unseen data and validated to prevent overfitting and confirm generalization. The models were trained on an Alienware Aurora R11 desktop computer (Dell Inc., Round Rock, TX, USA) with a 3.7 GHz 10-core Intel Core i9-10900K CPU (Intel Corporation, Santa Clara, CA, USA), 128 GB of DDR4-3200 MHz, and an Nvidia GeForce RTX 3090 GPU (Nvidia Corp., Santa Clara, CA, USA) at 150 epochs, with an early stopping patience of 50 and a batch size of 16.  
During validation, a suite of methods and metrics were applied to quantify model performance comprehensively. Intersection over Union (IoU) was used due to its relevance in object detection and segmentation tasks [30]. Additionally, the Matthew's correlation coefficient (MCC) was employed as it is particularly useful compared to F1 scores when dealing with imbalanced datasets without compromising results on balanced datasets [31]. MCCs provided a robust indication of pixel-wise classification accuracy by incorporating true and false positives and negatives, thus delivering a balanced metric that reflected both the presence and absence of the target condition effectively.  
To prepare the data for evaluation in the 3D space, manually segmenting the point clouds to only contain the targeted tote contents while retaining the outermost points on each axis was required to supply the ground truth point clouds without altering the depth map. The ground truth point clouds were then converted to depth map format for model training and evaluation. This was done using both Python (3.8.13) and Meshlab (Visual Computing Lab, ISTI-CNR, Pisa, Italy), where Python was used to generate the depth maps for the data, training, and testing of the models and Meshlab was used for the manual segmentation of the point clouds.  
Hue adjustment was the chosen augmentation technique during training to address the variability in color representations and ensure robustness to different color maps (cmaps) [32]. By modifying the hue values of the input images, the model could learn to be invariant to color shifts and changes, simulating a range of height conditions and camera settings. This form of color augmentation is critical in scenarios where the model is expected to operate in environments with varying distances from the cameras, where color is proportional to distance, or when the data are sourced from multiple devices with different profiles, such as the depth maps compared to RGB images [33; 34].
## 2 Materials and Methods
### Developing AR Model  
The 3D virtual models were generated from dental CBCT scans in DICOM format via a multi-step software pipeline. Initially, 3 Dslicer ([[https://slicer.org/](https://slicer.org/)]([https://slicer.org/](https://slicer.org/))) comprehensively segmented internal structures, creating a structured 3D dataset. Blender ([[https://www.blender.org/](https://www.blender.org/)]([https://www.blender.org/](https://www.blender.org/))) optimized this dataset, enhancing model quality and reducing unnecessary complexity. Finally, the segmented model was exported in STereoLithography (STL) format with distinct layers for further manipulation. For visualization and interaction within the Holoeyes XR application (version 2.6, Holoeyes Inc., Minato City, Japan), individual STL segments were imported. An immersive exploration of the model's intricacies was facilitated using a Meta Quest 2 VR head-mounted display (HMD) (Meta Platforms, Inc., build 68.0, Menlo Park, CA, USA [[https://www.meta.com/help/quest/articles/whats-new/release-notes/](https://www.meta.com/help/quest/articles/whats-new/release-notes/)]([https://www.meta.com/help/quest/articles/whats-new/release-notes/](https://www.meta.com/help/quest/articles/whats-new/release-notes/))). Figure 1 provides a visual representation of these processes.  
Figure 1: Generation and Optimization of a Virtual 3D Model from DICOM Data for AR Visualization. A 3D virtual model was created from CBCT scans using open-source 3 Dslicer and Blender. 3 Dslicer analyzed internal structures and generated 3D data. Blender refined the model’s quality. The model was exported as segmented STL files into Holoeyes XR application version 2.6 for visualization and interaction. A Meta Quest 2 VR headset enabled immersive exploration of the model’s intricacies. Key software utilized: 3 Dslicer (data generation), Blender (optimization), Holoeyes XR application (visualization), Meta Quest 2 headset (interaction).