esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Freeway Traffic Capacity: The Impact of Autonomous Vehicle Platooning Intensity
## 4 Case Study
### Simulation Framework and the Selection of Parameters  
\"Simulation of Urban Mobility\" (SUMO) is an open-source, portable, and comprehensive traffic simulation package specifically designed for handling large-scale networks. It operates at a microscopic level, meaning it considers individual vehicles and their interactions in great detail. SUMO is capable of simulating various modes of transportation, including pedestrians, and provides a wide range of tools for creating complex scenarios. Its flexibility and extensive feature set make it an invaluable tool for researchers and practitioners involved in urban mobility simulations. SUMO has been used in this study because it runs fast and it is portable. The software can be run from the command line, which increases the execution speed by leaving out slow visualization. Also, it allows the use of faster data structures, each adjusted to the current purpose, instead of using complicated and ballast-loaded ones.  
In this simulation framework, the choice of proper vehicle-following models for CAVs and HVs is one of the most important elements. The variations in car-following models play a significant role in the diverse outcomes predicted in previous studies regarding the impact of CAVs on traffic flow. The behavior of CAVs in car-following models is an area that is still undergoing development and refinement, and as a result, these models are not yet fully mature. The ongoing advancements and research in this field aim to improve the accuracy and reliability of car-following models for CAVs, which will ultimately lead to a better understanding of their effects on traffic flow. Most studies only simulate CAV by reducing the reaction time of the car-following models, regardless of the other properties of CAVs. Accurate models of the dynamic responses of these systems are needed to produce realistic predictions of their effects on capacity and traffic flow dynamics. Cooperative Adaptive Cruise Control (CACC) car-following models are always used to model the longitudinal control systems of CAV. A state-of-the-art model that has been calibrated on real experimental data was chosen to simulate CAVs in this study [14], and the default Krauss model is used as the car-following model of HVs.
# The Applications of Magnetic Particle Imaging: From Cell to Body
## 2 The Principle of Magnetic Particle Imaging  
The imaging principle of MPI is based on the nonlinear magnetization of SPIONs immersed in an alternating external magnetic field. The nonlinear magnetic properties of SPIONs can be described by the Langevin function. When an alternating magnetic field (AMF) is applied to SPIONs, the magnetic moments of those particles that are not magnetically saturated will change with the alternation of the field, while those of particles in the saturated state will not.  
To utilize the magnetic properties of SPIONs for imaging, the primary MPI system consists of three parts: the selection field, the drive field, and the receiving coil. The selection field is a time constant field with sufficient magnitude that is constructed of two homopolar magnetic fields, which possesses a field free region (FFR) in between. The drive field is an AMF to which the SPIONs in FFR respond. The response of SPIONs leads to the variation of magnetization. The receiving coil detects the change in magnetization.  
MPI detects the electronic moment of SPIONs [8]. When SPIONs are exposed to selection field, all particles except those in the FFR are saturated. When drive field is added onto those particles simultaneously with the selection field, only the direction of the magnetic moments of SPIONs in the FFR changes with that of the surrounding field. All other SPIONs in the selection field outside FFR will not response to the drive field because they are in the state of saturation. Those changes of SPIONs in FFR cause the oscillation of the magnetization, which induces a signal in the receiving coil. Then, the FFR moves within the region of interest to generate MPI images, by assigning the signal to an image location according to the corresponding location of FFR [8]. The reconstruction of MPI image requires algorithms such as X-space [20; 21; 26; 27; 28; 29; 30; 31; 32; 33].
## 4 SURAA: The Proposed Method and Tool
### Execution Phase  
#### 4.2.1 Dynamic Parallel Kernel  
Figure 5 further illustrates the allocation of various segments to different kernels. The segments with rows containing larger \(npr\) are executed as a part of the dynamic kernel, whereas the other segments are executed based on the average length of the rows in the segment. The segments that form the part of the dynamic kernel have been shown in red. In the figure, the segments from \(Seg_{1}\) to \(Seg_{I}\) have \(npr\) less than or equal to \(max\_child\_threads\) (The blue dashed line shows the \(max\_child\_threads\)  
Figure 5: SURAA: Segmentation, grouping, and allocation of kernels among the three groups for the proposed scheme.  
parameter) and hence will be grouped together in \(G_{1}\), which shall be executed by the dynamic kernel. The segment \(Seg_{k}\) has mean \(npr\) less than 32 and hence is executed by the scalar CSR based kernel. The rest of the segments, between \(Seg_{2}\) and \(Seg_{k}\) are grouped together into \(G_{2}\) and executed by the vector kernel.  
Algorithm 3 is the parent algorithm for the dynamic kernel that will execute on the segments of \(G_{1}\). This parent kernel assigns child kernels, depicted in Algorithm 4, to each row. Each thread of the parent kernel will work on a row by invoking child kernels. Multiple warps (depending upon the row size) of the child algorithm will execute the row assigned to them. As Multiple warps are involved for a row in the child kernel, we require a reduction within each warp as well as among the multiple warps. We use the CUDA function \(  
```
0:\(A_{csr}(val,row_ptr,col_idx),\ vector:x,\ vector:y,\ vector:perm\)
0:\(vector\ val:y[n]\)
1:functionChildrenKernel
2:Initialisation:\(sum-0,\ shared_mem[0..31]\gets 0\)
3:\(tid\gets thread\_1d\)
4:\(tid\gets thread\_1d\)
5:\(lid\gets tid\%32\)
6:\(threads\_total\gets blockDim\)
7:\(wid\gets tid/32\)
8:for\(i=tid;i<nnz;i+=threads\_total\)do
9:\(sum+=values[i]\times x[col\_idx[i]]\)
10:endfor
11:for\(i=16;i>0;i=i>>2\)do
12:\(sum+\leftarrow\_shfl\_down\_sync(0 xffff,sum,i)\)
13:endfor
14:if\(lid==0\)then
15:\(shared\_mem[wid]\gets sum\)
16:endif
17:\(sum+\sum\leftarrow\sum^{32}_{i=0}shared\_mem[i]\)
18:if\(id==0\)then
19:\(y[0]\gets sum\)
20:\(row\_id\gets perm[tid]\)
21:endif
22:endfunction
```  
**Algorithm 5** SURAA_Child_Kernel
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 3 Processed Data and SLR Validation
### SLR Validation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**PRN** & **Perc.** & **Num.** & **Overall** & **Noneclip.** & **Eclip.** & **NYS** & **YM** \\ \hline
**C01** & 99.6\% & 237 & \(-\)7.5/19.0 & \(-\)4.2/17.6 & \(-\)12.8/21.1 & \(-\)/\(-\) & \(-\)/\(-\) \\
**C08** & 96.6\% & 593 & \(-\)4.2/7.3 & \(-\)4.2/6.9 & \(-\)5.7/11.8 & \(-\)0.8/3.0 & \(-\)16.8/20.8 \\
**C10** & 95.5\% & 554 & \(-\)3.3/6.6 & \(-\)3.8/6/0 & \(-\)0.5/9.2 & \(-\)1.0/8.2 & 0.1/10.1 \\
**C13** & 97.9\% & 972 & \(-\)1.8/5.2 & \(-\)1.6/5.1 & \(-\)3.2/6.0 & \(-\)1.7/4.6 & \(-\)4.8/7.3 \\
**C11** & 97.8\% & 1712 & 0.9/3.4 & 1.1/3.4 & 0.4/3.2 & 0.9/3.1 & \(-\)1.7/3.8 \\
**C20** & 98.1\% & 808 & 0.3/4.6 & 0.3/4.5 & 0.2/5.0 & 0.3/4.7 & 0.2/5.7 \\
**C21** & 98.8\% & 1037 & 0.0/4.4 & 0.1/4.2 & \(-\)0.6/5.4 & \(-\)1.3/5.6 & 0.9/5.0 \\
**C29** & 97.6\% & 922 & \(-\)0.4/5.7 & \(-\)0.5/5.9 & \(-\)0.1/4.8 & 0.9/5.0 & \(-\)2.2/4.3 \\
**C30** & 99.1\% & 889 & \(-\)1.4/5.0 & \(-\)1.5/5.0 & \(-\)1.0/4.8 & \(-\)0.9/4.7 & \(-\)1.2/5.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The statistical information of the percentage of the available SLR observations, the number of the available SLR observations, and the Mean/RMS for SLR validation residuals (Unit: cm). The orbit accuracy of SLR validation is in at the decimetre level for GEO C01, while at the centimetre level for IGSO and MEO satellites. Therefore, we adopt different data preprocessing strategies and different statistical strategies for the SLR-only orbit determination of different BDS satellites, as described in Section 2.2. In addition, the percentage of qualified SLR observations is over 95.5% for all BDS satellites, indicating that the preprocessing strategies are reasonable and reliable, especially for BDS-2 GEO C01 and BDS-3 satellites.
# Camera-Based Vital Sign Estimation Techniques and Mobile App Development
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Image Sequence Used** & **Devices Used** & **Illumination** \\ \hline \multirow{3}{*}{ICA [2,3]} & 640 \(\times\) 480 pixels, 24 bit & \multirow{3}{*}{Indoors, sunlight} \\  & 20 s (15 frames/s) & & \\  & 12 subjects (aged from 18 to 31) & & \\ \hline \multirow{3}{*}{AGRD [6]} & 640 \(\times\) 480 pixels, 24 bit & \multirow{3}{*}{Fluorescent lamp} \\  & 20 s (30 frames/s) & & \\  & 10 subjects (aged from 20 to 33) & & \\ \hline \multirow{3}{*}{CHROM [9]} & 1024 \(\times\) 752 pixels, 8 bit & \multirow{3}{*}{-} \\  & 25 s (20 frames/s) & & \\  & 117 subjects (unknown ages) & & \\ \cline{1-1} \cline{3-4}  & 768 \(\times\) 576 pixels, 8 bit & & \\ \cline{1-1} \cline{3-4} POS [10] & 1125–2700 s (20 frames/s) & & \\  & 5–16 subjects according to experiments & & \\  & 60 video sequences (unknown ages) & & \\ \hline \multirow{3}{*}{Proposed} & 640 \(\times\) 480, 852 \(\times\) 480, 1280 \(\times\) 720, 1080 \(\times\) 1920 & \multirow{3}{*}{3200 K and 5600 K fluorescent lamps, sunlight} \\  & 7 s (30 frames/s) & & \\ \cline{1-1} \cline{3-4}  & 20 subjects, 508 video sequences (aged from 31 to 61) & & \\ \cline{1-1} \cline{3-4}  & 640 \(\times\) 480, 852 \(\times\) 480, 1280 \(\times\) 720, 1080 \(\times\) 1920 & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\ \cline{1-1} \cline{3-4} Proposed & 7 s (30 frames/s) & & \\ \cline{1-1} \cline{3-4}  & 20 subjects, 508 video sequences (aged from 31 to 61) & & \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of experimental conditions used when testing various noncontact HR detection methods. This paper proposes a non-contact method for detecting HR, SpO2, and RR by combining the POS method with a color difference signal amplification technique. Additionally, a time-shifted moving average process is proposed to eliminate temporal noise such as lighting flicker, along with a remeasurement algorithm based on heart rate comparison over time intervals to improve HR detection accuracy. The proposed method was validated for robustness against variations in image quality and lighting using video sequences recorded with various types of smartphone cameras and laptop webcams. This paper is organized as follows. Section 2 introduces the contactless detection methods for HR, SpO2, and RR based on smartphone camera image sequences. Section 3 describes the recorded image sequence database and verifies the detection performance of the proposed method using Bland-Altman plots. It also presents the developed mobile application and analyzes the time cost and noise effects associated with the proposed method. Section 4 introduces the challenges related to cameras and video in non-contact bio-signal research, the lack of diverse ethnic databases, and future plans.
## 3 Results and Discussion
### Range Analysis and Analysis of Variance  
The rationale behind the experimental design mainly included two aspects: one was to emphasize the integrity and efficiency of the experimental plan (i.e., how to conduct effective and economical experiments for the purpose of the experiment), and the other was concerned with the analysis of experimental observations, mainly by analyzing the data obtained from the experiment by appropriate methods, and then judging the results. Therefore, for each experimental factor, its impact on the performance of LPC was evaluated using range analysis and analysis of variance. Basically, this was achieved by separating the total variability of the S/N ratios into contributions by each of the process parameters and the error [71]. Table 12 lists the 28-day test results of the LPC and the corresponding S/N. Furthermore, we calculated the average value of the S/N ratio of each factor at the same level and calculated the main effect value (delta) of the factor level. In this way, these data can be made into an auxiliary table, as shown in Table 13. On the other hand, the  
Figure 8: The test results of LPC: (**a**) flexural strength and (**b**) splitting tensile strength.  
relative contribution of different control factors in describing the mechanical properties of LPC mixtures was performed using analysis of variance (ANOVA) tests [72]. In addition, the application of the statistical \(F\) test can determine which process parameters had a significant impact on performance characteristics. The results of the ANOVA and \(F\) test are represented in Table 14. Among them, the contribution of each factor was obtained by dividing the sum of the squares of each factor by the total sum of the squares.
# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 2 Preliminaries
### Multiple Input Channel  
It can be observed from the ASM that the image is mainly blurred due to atmospheric light and particle scattering. Based on these phenomena, [PERSON] et al. [21] proposed GFN, which inputs three different image processing methods, namely, white balance to deal with color deviation, contrast enhancement to increase the structure of objects, and gamma correction to solve dark regions. Then, they are concatenated to form a 9-channel input. The dehazing image is expressed by  
\[J=C_{wb}\circ I_{wb}+C_{cc}\circ I_{ce}+C_{gc}\circ I_{gc}, \tag{3}\]  
where \(C_{wb}\), \(C_{ce}\), and \(C_{gc}\), are confidence maps for the white-balanced image, \(I_{wb}\), the contrast enhanced image, \(I_{ce}\), and the gamma corrected image, \(C_{gc}\), respectively, and \(\circ\) is the Hadamard product.  
In addition to image pre-processing, a multichannel input can also be mapped to different color spaces to increase the number of channels in the input image. [PERSON] et al. [22] emphasized that the halo effect can be suppressed in the HSV color space. [PERSON] et al. [23] experimentally proved that better contrast and brightness can be obtained in the YCbCr color space. Therefore, inspired by [22; 23], [PERSON] et al. [20] used a multi-cue color space, including RGB, HSV, YCbCr, and Lab, to formulate a 12-channel input.  
The proposed method integrates the concepts of GFN [21] and TheiaNet [20] to preprocess hazy images. In the next section, we introduce significant blocks of our image-dehazing CNN, including the image pre-processing blocks, inception-like blocks, SPP blocks, and attention gates (AG).
## 3 Data Acquisition and Processing  
Due to the large difference in height of steep cliffs, which is close to 200 m, it is difficult to obtain complete point cloud information by a single technical means. We, therefore, adopted an integrated ground-air approach to acquire the point cloud data. In this paper, the ground 3D laser scanning point cloud data were mainly used with high accuracy, and the point cloud data from UAV tilt photogrammetry were also collected to supplement. Then, we performed data fusion of the two point clouds to achieve mutual complementation of different data sources and to ensure their integrity. The fused point cloud data can fully reflect the terrain features. The process of point cloud acquisition is shown in Figure 3.
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 4 Result and Discussion
### Select the Optimal Hyperparameters of EMNP-SPI  
This hyperparameter is determined by simulation experiments. We perform experiments on parameters ranging from 0.1 to 1 using the flower dataset. Since we are mainly concerned with the image reconstruction quality at low measurement rate (MR), the MR is set to 0.05 in the simulation experiments. Firstly, we set a = 1, with b ranging from 0.1 to 1. The PSNR of the reconstructed image is shown in Table 1. It can be seen from Table 1 that when b is 0.3, the PSNR of the reconstructed image is highest. The optimal value of b is 0.3. Then we set b = 0.3, with a ranging from 0.1 to 1. The PSNR of the reconstructed image is shown in Table 2. It can be seen from Table 2 that when b is 0.3, the PSNR of the reconstructed image is highest. Through simulation experiments, it can be confirmed that the optimal values of the hyperparameters a and b of the EMNP-SPI method are 0.3.
## 7 Performance Results of Proposed Algorithms  
The addition of the SDN in experimental setup II is realized by inserting the OVS switch in the data path between the sending node and the receiving nodes. This allows the  
Figure 9: The Tuning algorithm flowchart.  
SDN controller to execute the proposed algorithms, which run as a Ryu application, select the energy-optimized combination of interfaces, and communicate the selection to the OVS to implement it.  
Figures 10 and 11 show the energy differences after the Interface Selection algorithm and Tuning algorithm for the Cubic and BALIA congestion control algorithms, respectively.  
Figure 10 shows that the Tuning algorithm is successful in reducing energy consumption, especially when the number of nodes increases. This is due to the fact that increasing the number of nodes leads to decreasing the throughput values of both interfaces. Since the throughput is also affected by the channel condition of each interface, this leads to a higher possibility of having nodes with both interfaces achieving close throughput values, which requires the Tuning algorithm to make a fine adjustment to the decision of the IS algorithm. Similarly, in the case of the BALIA algorithm, the Tuning algorithm shows, in general, reduced energy consumption when increasing the number of nodes, as depicted in Figure 11. The reduction in energy consumption after using the DNN-based Tuning algorithm, as presented in Figures 10 and 11, appears to be limited since we use a 60 MB file in the conducted experiments to finish every sample run in a reasonable time. The larger the amount of downloaded data, the greater the energy saving achieved by using the Tuning algorithm per node, which leads to considerable overall energy savings for all battery-powered nodes across the network, given that running the Tuning algorithm does not affect the energy consumption of battery-operated devices.  
Figure 12 shows a comparison of the average throughput of the Cubic algorithm in various scenarios, with and without the proposed SDN-based scheme. These scenarios involve using either single-path TCP (SPTCP) over a single interface (LTE or WiFi) or both interfaces for all nodes. Figure 13 displays the energy consumption results for the same scenarios as Figure 12. It is clear from both figures that using the proposed algorithm leads to increasing throughput and decreasing energy consumption compared with the other studied scenarios. Similarly, Figures 14 and 15 show that the proposed algorithms are able to achieve higher throughput and better energy consumption.  
Figure 12: Comparative average throughput using Cubic algorithm.  
Figure 13: Comparative energy consumption using Cubic algorithm.  
Tables 2 and 3 summarize the throughput and energy consumption performance comparison, respectively, of the proposed SDN scheme when it is applied with BALIA and Cubic compared with MPTCP BALIA (without the SDN scheme), MPTCP Cubic (without the SDN scheme), LTE (SPTCP), and WiFi (SPTCP).
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 5 Detailed Elaboration of Each Layer
### Functions of User Terminals
#### 5.3.1 User Access Stratigies and Procedures  
In the MLSCS, the signaling channel employs a scheme that combines on-demand access (DA) and random access (RA) generally, while the service channel uses only on-demand access. Consequently, the access methods and procedures for the signaling channel and service channel differ.  
Signaling channel access procedure: Signaling channel access combines DA and RA. Different user types and service types require different access methods, with corresponding access procedures. Figure 5 illustrates the access procedure for the signaling channel. To apply for the signaling channel, the user first submits a request to SBS. Based on the user and service type, the SBS determines whether to use random or on-demand access. For random access, the SBS checks for any idle random access channels. If an idle channel is available, it is allocated to the user. If no idle channels are available, the access procedure is restarted after a timeout. For on-demand access, the SBS checks the occupancy of reserved or authored channels. If an idle channel is available, it is allocated to the user. If no idle channels are available, the user waits until a channel becomes idle and is then allocated for access.  
Service channel access procedure: The service channel access procedure is illustrated in Figure 6. Spectrum/channel allocation is based on service priority to ensure that high-priority and online users' services can be successfully accomplished and that the QoS is guaranteed. When channel resources are limited, low-priority users or users with low-priority services must compete for access to the system. The user access order is determined based on their priority and the results of any games or competitions that may be used to allocate the limited resources.  
Figure 5: Signaling channel access procedure.
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 3 Results
### Evaluation of Sharpening Quality
#### 3.1.1 Quantitative Comparison Using Synthetic Data  
A comparison of six different leading methods using six quantitative image quality metrics is shown in Table 1. Per-band image quality is compared using signal-to-reconstruction error ratio (SRE) [31], Root-Mean-Square-Error (RMSE) [63], Relative dimensionless global error (ERGAS) [64], Structural Similarity Index Measure (SSIM) [65], and Universal Image Quality Index (UIQI) [66]. Spectral quality is measured using Spectral Angle Mapper (SAM) [67]. Four synthetic S2 images from [30] were processed and evaluated against ground truth. They present varied scenes with coastal, agricultural, rural, and urban geographies. No specific parameter tuning for the different images was performed for any of the methods, including the proposed method. Each image is processed using the default parameters. A combined mean of 10 runs of the four synthetic images is given for the proposed method and SUSS. Other methods are not randomly initialized and give identical results for the same inputs, so an average of the metrics for the four images is shown.  
As shown in Table 1, our proposed method demonstrates superior performance across all evaluated metrics compared with state-of-the-art Sentinel-2 sharpening methods, with one exception. S2 Sharp achieves the same SSIM value when measuring just the 20 m bands.  
Figure 4: Natural color images of the datasets used for assessing the quality of S2 sharpening, generated from the red, green, and blue bands B4, B3, and B2. The first four are synthetic datasets generated from Aviris images that show coastal, agricultural, highland, and urban geographies; the final image is a view of Reykjavik as captured by Sentinel-2.  
A distinctive feature of our method is its consistent performance across both spatial resolutions. While competing methods exhibit significant variation in effectiveness between 20 and 60 m bands, our approach maintains comparable quality metrics for both resolutions. This consistency is particularly noteworthy in the 60 m bands, where competing methods show marked degradation in performance. S2 Sharp is the only other method that comes close to a similar consistency across both resolutions, though with lower overall performance metrics.