esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Beam-Hopping-Based Global Beam Scheduling and User Resource Allocation Algorithm
### Algorithm C: Joint Frequency-Power Allocation Algorithm  
This section presents Algorithm C, which performs the joint optimization of frequency and power resources based on the overall objective defined in objective function P2. For each beam, the total bandwidth is equally divided into \(U_{t,b}\) sub-bands, where \(U_{t,b}\) is set as equal to the number of users \(N_{t,b}\) served by the \(b\)-th beam in the \(t\)-th time slot, resulting in a sub-band set \(\{B_{f}^{sub}|f=1,2,\ldots,U_{t,b}\}\). For each user \(u_{t,b,f}^{i}\), the achievable data rate \(R_{t,b,f}^{i}\) across all sub-bands \(B_{f}^{sub}\) is computed and used to construct a reward matrix. The Hungarian algorithm is then applied to solve the resulting assignment problem, aiming to maximize the total system throughput. The frequency allocation problem follows the objective in objective function P2 and is subject to constraints (7i) and (7g).  
For power allocation, we first consider intra-beam power distribution among users. Given the beam-specific channel parameters and the total power \(P_{t}^{b}\) allocated to the \(b\)-th beam in the \(  
algorithm, and \(\log_{2}\lambda_{b,f}^{i}\) and \(U_{t,b}\log_{2}(U_{t,b})\) are constant terms independent of the optimization variable. Since \(\log_{2}(P_{t}^{b}+S_{t,b})\) is a strictly concave function over its domain and both \(P_{t}^{b}>0\) and \(S_{t,b}>0\) hold, the objective remains concave. Moreover, the constraints of the optimization problem (7d) and (7e) are linear and do not affect the concavity of the objective. The form of this problem is consistent with the power allocation formulation in [23], and can therefore be efficiently solved using standard convex optimization methods.
## 3 Results  
### Simulation Verification  
The simulations were verified by comparing the measured \(HVL\) values with those determined in the simulations. The results of the comparison for the selected tube voltages and anode-filter combinations are presented in Table 4. In the simulations carried out using the GEANT4 code, the range of the secondary radiation is determined by the cut value parameter. In this work, secondary radiation consisted of photons, electrons, and positrons produced as a result of the electromagnetic interactions of X-rays from an X-ray tube. In all the simulations, the cut value parameter was set at 0.001 mm. This value allowed for high accuracy in calculations within a reasonable simulation time.  
Based on the results presented in Table 4, it can be inferred that the \(HVL\) values determined using the measurement method were consistent with simulation results taking into account the uncertainties.
## 3 Influence of Physical Properties of NS on Communication, Navigation, and TT&C Integration
### Atmospheric Loss in NS  
The airspace range of the NS includes the troposphere, stratosphere, and part of the ionosphere. When radio waves pass through the troposphere, the signals are most affected. They will be absorbed and scattered by oxygen molecules, water vapor molecules, as well as clouds, fog, rain, and snow in them, thus causing losses to the signals. This kind of loss is closely related to the radio wave frequency, the elevation angle of the beam, the weather conditions, the geographical location, etc. For communication systems with frequencies below 1 GHz, the influence of the troposphere can be almost ignored. However, for the occasions where high-frequency communication is adopted in the NS, it should be given due attention [73]. Specifically, the impacts of tropospheric atmospheric loss on NS vehicles include cloud and fog attenuation, gas absorption, atmospheric scintillation, atmospheric refraction and depolarization, etc. Previous studies have shown that unless there are instantaneous heavy rain and snow conditions, there is no need to consider the impact of depolarization on signal propagation [74].
## 3 Results
### Validation Saddle Pressure Measurement and Bike Fitting
#### 3.6.1 Test Subject A  
The initial measurement of test subject A yielded a maximum pressure of 877 mbar in the left half of the saddle, while the right side exhibited a smaller area with a peak pressure of 757 mbar. (Figure 11a). Consequently, the pressure was higher on the left half of the saddle while simultaneously occupying a larger area. In accordance with the recommendations of the bike fitting specialist, it is likely that the left leg of subject A is somewhat shorter than the right. This discrepancy would result in increased pressure on the left half of the saddle. The lower-pressure areas were distributed symmetrically and evenly in a forward direction along the length of the saddle.  
In the validation measurement, the peak pressure on the left side was reduced by approximately 15% and the right side by 17% compared to the initial measurement, as can be observed in Figure 11b. As a result of the modifications made to the area surrounding the peak pressure in order to guarantee sufficient stability, it is plausible that these areas are responsible for generating the peak pressure observed in this figure. It seems reasonable to posit that this transition (at 700 to 600 mbar) could be made even smoother, with a lower strut thickness at 600 mbar.  
The histogram (Figure 11c) depicts the pressure distribution of test subject A, with the data derived from the two distinct measurements. The horizontal axis represents different pressure ranges in mbar, while the vertical axis shows the frequency of occurrence of these pressure values. Two bars are displayed for each pressure range, a gray bar for the initial measurement and a black bar for the validation measurement. At the outset, within the lowest pressure range of 1 to 75 mbar, the highest values are exhibited for both measurements. The frequency observed in the validation measurement is markedly lower  
Figure 11: Visual representation of both measurements of test subject A; (**a**) color plot with peak pressure values of the initial measurement, (**b**) color plot with peak pressure values of the validation measurement – blue, low pressure to orange, high pressure, and (**c**) histogram of both measurements.  
than that observed in the initial measurement. Subsequently, the frequency of the pressure values in the validation measurement increases and the validation measurement largely adheres to the frequency patterns observed in the initial measurement. It is evident that the frequencies of the validation measurement exhibit a notable increase in the ranges of 351 to 375 mbar and 401 to 425 mbar. Moreover, the maximum pressure values are observed within the maximum range of 751 to 775 mbar. In contrast, the initial measurement also demonstrated a deviation in the frequencies within the range of 876 to 900 mbar. The adapted saddle pad resulted in a slight improvement in peak pressure values for subject A. The ranges above 775 mbar are entirely absent in this instance.
## 3 Results
### Evaluation of Detection Accuracy Using Additional Metrics
#### 3.2.2 DSC Above Threshold Rates  
The DSC metric provided an additional perspective on spatial overlap. The results, as shown in Table 3, indicate that the mean DSC values were 79.0% for the RCA, 81.1% for the LCA-LAD, and 73.8% for the LCA-CX. Similar to the IoU results, the LCA-LAD demonstrated a superior performance in terms of overlap, achieving its highest fold-wise value of 85.5% in fold4. The RCA also exhibited a strong performance, with a peak DSC of 84.0% in fold3. In contrast, the LCA-CX had the lowest mean DSC, with fold4 showing the weakest performance at 66.1%. These results highlight a consistent pattern where the LCA-LAD and the RCA achieved higher spatial overlap compared to the LCA-CX.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **RCA** & **LCA-LAD** & **LCA-CX** \\ \hline fold1 & 0.71 & 0.67 & 0.63 \\ fold2 & 0.72 & 0.62 & 0.61 \\ fold3 & 0.70 & 0.70 & 0.64 \\ fold4 & 0.72 & 0.78 & 0.57 \\ fold5 & 0.68 & 0.72 & 0.62 \\ \hline mean & 0.71 & 0.70 & 0.61 \\ \hline \end{tabular}
\end{table}
Table 1: Average Precision (AP) for RCA, LCA-LAD, and LCA-CX Across Five Folds.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **RCA** & **LCA-LAD** & **LCA-CX** \\ \hline fold1 & 77.5 & 76.7 & 76.7 \\ fold2 & 76.3 & 77.2 & 73.6 \\ fold3 & 82.0 & 83.1 & 74.8 \\ fold4 & 78.9 & 84.2 & 64.2 \\ fold5 & 71.3 & 75.2 & 66.4 \\ \hline mean & 77.2 & 79.3 & 71.1 \\ \hline \end{tabular}
\end{table}
Table 2: IoU Above Threshold Rates for RCA, LCA-LAD, and LCA-CX Across Five Folds.
# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 4 Network Generalization Experiments
### Wavelet Prediction of Measured Data  
In order to explore the practicability of this method, a group of measured data are used to test the performance of the above method for wavelet extraction. Several landmines are buried in red clay and clay scenes for experiments, and the LTD series GPR of China Research Institute of Radio wave Propagation (CRIRP) is used to collect echo data. Red clay is a soil with high water content formed by carbonate weathering and has a large relative permittivity \(\varepsilon_{r}=12\). The water content of the clay is low, and the relative permittivity \(\varepsilon_{r}=12\) is low. The parameters of red clay and clay are listed in Table 2. There are 893 and 692 channels of A-scan data from red clay and clay scenes, respectively, each with 1024 sampling points, as shown in Figure 14. Different background media cause different attenuation of EM waves in two scenarios. Therefore, the echo data are significantly different.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Source** & \(\varepsilon_{r2}\) & \(\varepsilon_{r3}\) & \(d\) **(m)** & \(R\) **(m)** \\ \hline
5 & [PERSON] & 5 & 8 & 0.3 & 0.2 \\
6 & [PERSON] & 10 & 13 & 0.3 & 0.2 \\
7 & [PERSON] & 9 & 1 & 0.24 & 0.16 \\
8 & Gaussian dot norm & 5 & 7 & 0.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of derivative models 5–8.  
Figure 13: The predicted wavelet of derived models 5–8.  
The antenna parameters of the radar system are different from the simulation model. The radar system parameters often affect the wavelet prediction network. If you want to use the network trained with the simulation data to predict the wavelet of the actual measurement data, you must accurately set the antenna parameters in the simulation model. However, it is difficult to measure the system parameters of the actual detection equipment accurately. Moreover, system components, noise, and the internal interference is difficult to be reproduced ideally. According to the learning characteristics of LSTM network for sequence correlation, we try to train the network directly with the measured data.  
A total of 38 channels of A-scan data of the red clay detection are randomly selected to splice in sequence as the training set. The training process is the same as above and ends when the loss is less than 0.0001. Then, another 12 different channels of A-scan data are randomly selected to predict the wavelet, as shown in Figure 15. It can be seen that the network can well predict the wavelet whether the echo contains the target signal or not. The strong signal at the 200-th track of the blue line corresponds to the reflected signal from the mine. The LSTM network can separate the wavelet from the target signal and environmental interference. Then, the trained network with red clay data is directly used to predict the wavelet of the clay scene, as shown in Figure 15b. The predicted wavelet is close to the A-Scan without the target signal, which is the ground truth of the wavelet for the clay background.
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 4 Antenna Feeding for Fabric Antennas  
As illustrated in the previous section and by most fabric-based antenna research, the SMA port connection either by probe or edge feeding [19] is the commonest method to feed the fabric patch antenna and achieves somewhat of an ideal performance without any real-world application intended. In that respect, to properly and efficiently achieve smart clothing applications, the ergonomic features of the antenna must be taken into consideration. Alternate feeding methods include a stripline approach that feeds the radiators directly with a printed stripline, with the obvious drawback being spurious surface current induced between the ground and the radiator on the same plane.  
The third and less common method is the aperture feeding technique using dielectric-induced resonators and/or reflectors. This technique provides a slight advantage over the conventional SMA ports and the stripline methods but with a high level of complexity and antenna thickness making it unattractive. For instance, the most recent design iteration reported in [20; 21; 22] shows a five-layer antenna with the first part comprising the patch, substrate, and ground with an aperture slot. This first part is entirely fabric based. Attached to the ground is the second part, made of printed circuit board (PCB) feed isolator/substrate and microstrip line (on the PCB backside) with a soldered coaxial cable.  
The currents flowing through the microstrip line is induced onto the ground aperture, designed to resonate at certain wavelengths to feed the top patch. Although not a truly fabric-based solution with the inclusion of the PCB, miniaturization techniques in [20] enable the second part to be small compared to the antenna size. This method also provides wider bandwidths in certain cases. However, the risk of failure becomes high as special care is required to solder and/or attach the PCB to the fabric ground layer without burning the cloth while minimizing the air gaps that can potentially result in impedance mismatches. In addition, the PCB inclusion presents a similar ergonomic discomfort to the cloth end-user as both PCB and SMA poke into the undergarment.  
In this section, the authors experimented with the aperture feeding technique but implemented it in a unique way to suit printed fabric antennas with minimal discomfort.
# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 5 Datasets
### Datasets Summaries  
Through our extensive analysis, we identified a total of 25 datasets, which are detailed in Tables 1-3 presented below. Notably, 14 of these datasets have not been included in previous surveys, highlighting significant additions to the existing body of resources. This expanded collection provides researchers with a broader spectrum of data sources, facilitating more robust and diverse evaluations in the field of 6D pose estimation. \begin{table}
\begin{tabular}{l c c c} \hline
**Dataset Name** & **Type** & **Number of Images** & **Characteristics** \\ \hline
[78] Air Filter & Real & Not specified & Pose estimation for maintainability testing \\ \hline
[59] Apollo-SouthBay & Real & Not specified & Point cloud registration evaluation \\ \hline
[65] CAMERA25 & Synthetic & 25,000 & Overcome the challenge of annotating real-world data for 6D object pose and size estimation \\ \hline
[88] Cityscapes & Real & 5000 finely annotated, 20,000 coarsely annotated, 1525 test images & Autonomous driving in urban environments \\ \hline
[64] Cropped Linemod & Real & 15,783 images (derived from Linemod) & 6D object pose estimation with reduced background clutter \\ \hline
[38] GenMOP & Real & \(\sim\)4000 images & General model-free object pose dataset with 10 diverse objects, captured in varying lighting and backgrounds, with manually labeled keypoints for cross-sequence alignment \\ \hline
[89] HomebrewedDB & Real & 34,830 annotated frames & Addresses key challenges in the field, including scalability, training from 3D models, and robustness to occlusions, lighting changes, and object appearance variations \\ \hline
[48] Ex-vivo & Real & & Suture needle tracking in minimally invasive robotic surgery \\ \hline
[90] Fraunhofer IPA Bin-Picking (IC-BIN) & Both & 520 real-world point clouds, 206,000 synthetic scenes & 6D object pose estimation and instance segmentation for bin-picking tasks \\ \hline
[91] ITODD & Both & Not specified & 6D pose estimation and 3D object recognition in industrial scenarios \\ \hline
[92] KITTI & Real & Not specified & Advances research in autonomous driving perception systems \\ \hline
[93] Linemod & Real & 15 video sequences, each over 1100 images & 6D pose estimation for texture-less objects in cluttered scenes \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of Datasets for 6D Pose Estimation (Part I).
# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 3 The Proposed DCT-Based DFC System  
Figure 1 depicts the block diagram of the proposed DFC system. Fundamentally, in a DFC system, the camera is frontally aligned with the transmitter screen. First, the input image is converted to the frequency domain using the DCT. At the same time, the binary input data, \(b\in\{0,1\}\), are channel-encoded and modulated to symbol \(d\) (or \(s\)) and embedded into the image. The modulated input data are embedded into the frequency domain of the image via addition allocation. Subsequently, the data-embedded image is converted back to the spatial domain to be displayed on the screen. This operation is performed by inverse DCT (IDCT). At the same time, to correctly decode the data at the receiver end, a reference frame per data frame is sent. In this fashion, data-embedded frames and reference frames are multiplexed and repeatedly displayed on the screen.  
At the receiver end, the camera captures the image frame. However, due to the optical wireless channel and background noise, distorted images are received, which need to be recovered. To remove the distortion from the captured images, deep learning object detection from computer vision is used. Consequently, restored images are converted to the frequency domain. Then, utilizing the reference images and subsequent operations of subtraction, demodulation, and channel decoding, output data bits are estimated at the camera decoder.  
In this paper, we use RGB color images for data embedding. The RGB image is separated into three different channels, i.e, red (R), green (G), and blue (B). For each RGB channel, the above procedure was applied in the same way. In the next section, we describe the mathematical model of our proposed scheme. Then, in subsequent sections, we describe the experimental setup of the proposed scheme with the experimental results.  
Figure 1: Comprehensive block diagram of the proposed DFC scheme.
### Remote Sensing Perspective
The inclusion of other data sources, such as GEDI or UAV data further expands the potential of RS for mapping smallholder agriculture and cropping systems in West Africa [22; 46]. The need for improvements in cropland area assessment in this region, coupled with the potential for improvements made possible by using higher resolution data, also increases the need for computational resources, new methods, and technical skills for effective processing and analyses [47]. Recent studies have supported the hypothesis that data fusion can improve crop mapping [48; 49]. The success of RS applications hinges on both the quality of the data and the analytical techniques used. Machine learning (ML) offers a range of techniques, including conventional models like Random Forest, Support Vector Machine, Decision Trees, and k-nearest neighbors. In contrast, Deep Learning is a specialized subset of ML that focuses on models like (convolutional) neural networks (CNN) and multi-layer neural networks for effectively analyzing big datasets and complex tasks [40; 50].
## 6 Results and Discussion  
Here, in the present paper, the authors have discussed the performance of a public address system through a reliability approach. A mathematical model was developed by considering the different components of the PAS. The following are the pointwise results summary for the above.  
* The PAS availability behavior is shown Figure 4. One can observe that the availability of the PAS is constant with a value of 0.9998 during the observed time.
* Figure 5 reflects the PAS reliability behavior with a time unit. The decrease in reliability is quite smooth and later on becomes almost constant. At 20,000 h, the reliability of the PAS becomes just 0.0321.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time (h)** & \(\begin{array}{c}\mathbf{E_{P}}(t)\\ \mathbf{K_{2}}\textbf{=1}\end{array}\) & \(\begin{array}{c}\mathbf{E_{P}}(t)\\ \mathbf{K_{2}}\textbf{=2}\end{array}\) & \(\begin{array}{c}\mathbf{E_{P}}(t)\\ \mathbf{K_{2}}\textbf{=3}\end{array}\) & \(\begin{array}{c}\mathbf{E_{P}}(t)\\ \mathbf{K_{2}}\textbf{=4}\end{array}\) \\ \hline
0 & 0 & 0 & 0 & 0 \\ \hline
2000 & 17997.93 & 15997.93 & 13997.93 & 11997.93 \\ \hline
4000 & 35995.86 & 31995.86 & 27995.86 & 23995.87 \\ \hline
6000 & 53993.80 & 47993.80 & 41993.80 & 35993.80 \\ \hline
8000 & 71991.73 & 63991.73 & 55991.73 & 47991.73 \\ \hline
10000 & 89989.66 & 79989.66 & 69989.66 & 59989.66 \\ \hline
12000 & 107987.60 & 95987.60 & 83987.60 & 71987.60 \\ \hline
14000 & 125985.53 & 111985.53 & 97985.53 & 83985.53 \\ \hline
16000 & 143983.46 & 127983.46 & 111983.46 & 95983.46 \\ \hline
18000 & 161981.40 & 143981.40 & 125981.40 & 107981.40 \\ \hline
20000 & 179979.33 & 159979.33 & 139979.33 & 119979.33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Expected profit from the PAS.  
Figure 8: Expected profit from the PAS.  
* Figure 6 shows the variation in the mean time to failure of the PAS concerning variation in different failure rates. It is quite clear from the graph that the system's MTTF is lower with respect to the microphone compared to other failures.
* Figure 7 shows the partial derivatives of the system reliability \(R(t)\) with respect to four different failure rates \(\lambda_{mic}\), \(\lambda_{mix}\), \(\lambda_{amp}\), \(\lambda_{sp}\) at various time points (in hours). A negative partial derivative implies that an increase in the corresponding failure rate parameter leads to a reduction in the system's reliability, while a positive partial derivative would indicate the opposite. It is quite clear from the graph of the sensitivity of the reliability that the system's reliability is very sensitive with respect to the failure rate of the microphone, mixer, and amplifier.
* Figure 8 shows the expected profit of the public address system at different time points (in hours) for different values of the parameter K\({}_{2}\). It is quite clear that the profit decreases with the increase in the service cost K\({}_{2}\). Such information can be crucial for decision-making, financial planning, and resource allocation in optimizing the performance of the system.