esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Proposed Multiconstellation Positioning Algorithm  
The proposed positioning algorithm calculates the navigation information i.e., position, velocity etc. of the object where antenna is fixed. In this paper, BeiDou and GPS satellite observations are used in combination as a multiconstellation GNSS system. Let \(n\) be the number of observations from the first constellation and \(m\) be the number of observations from the second constellation (first can either be any GPS or BeiDou and other is the second). The first step involves the selection of subsets. In classic subsampling based estimation schemes, for a statistical data analysis, a small number of subsamples are drawn in random to process from all possible combinations and each subsample contains a minimum required number of observations. Subsamples are drawn in random because of large number of data samples and is not possible to process all combinations. In contrast, GNSS observations are finite and by using a random technique, it is possible that a good observation is thrown away. Hence it is more appropriate to consider all observations. If there are \(\overline{N}\) number of combinations from all \(n+m\) observations with \(n_{sub}\) observations in one combination, there will be \(\overline{N}\) possible estimates \(\overline{\mathrm{G}}=\left\{\hat{X}_{1},\hat{X}_{2},\ldots,\hat{X}_{\overline {N}}\right\}\). In this process, \(n_{sub}\) is an essential parameter that needs to be chosen carefully because the position estimation time is directly related to number of subsamples. For example, if there are 20 satellite observations are available from two constellations and 5 unknowns (three positions, two clock corrections) are required to be estimated, minimum 5 observations are required for estimation of unknowns and there will be 15,504 number of combinations and it is not possible to process all combinations in the GNSS receiver. Using hit and trial approach, it is found that it is possible to relate the sample size \(n_{sub}\) to \(CNo\) such that the \(n_{sub}=(n+m)-k_{CNo<thr}^{obs}\cdot k\) is the number of observations having \(CNo\) smaller than a defined threshold.  
Because the presence of contaminated observations, the final position estimate \(\hat{X}_{rb}\) must meet the following condition,  
\[\sum_{l=1}^{n+m}\mu\left(\frac{r_{l}\left(\hat{X}_{rb}\right)}{s_{rb}}\right) =\min \tag{22}\]  
\(s_{rb}\) is the corresponding scale factor to the robust estimate \(\hat{X}_{rb}\).  
The proposed estimation procedure attains the required accuracy and consistency in two stages. The first stage is aimed to compute a reliable initial estimate. The second stage tries to attain the required accuracy. The two-stage algorithm is described in the following.
# Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks
## 5 Defect Detection
### Architecture  
Given the complexity of the task due to non-uniform illumination, inconsistent image sharpness, varying road conditions, patches partially including the side of the road and so forth., a deeper ConvNet architecture is necessary than for RSN. In our previous research [12], a ResNet based on transfer learning [26] architecture was found to attain satisfactory results for situations in which the patches were sampled from roads with less ambiguous defects and better lighting conditions. However, our further developments on similar datasets have shown that there is a possibility of obtaining slight performance gains by utilizing the contextual area surrounding each of the patches within the orthoframe [27]. By extracting relevant features from not only the patch but also its surroundings, we can train a classifier better equipped to deal with difficult cases in which more contextual information is needed for an accurate classification (such as when a pavement crack lies at the very edge of the patch).  
In order to extract helpful contextual features to the ConvNet, our approach is to introduce an auxiliary input stream for the feature extraction part of the network. Thus, the ConvNet operates with content-context input pairs in which the classification label is determined solely by the content input. An illustration of how this pair is extracted from an orthoframe is given in Figure 6.  
Figure 5: Road area image pre-processing.  
The extracted features are then flattened and concatenated to attain a fully connected layer of length 8192, with the content and context streams contributing an equal amount of neurons to the layer. These neurons, each with their respective weights, are then fed to an additional fully connected layer of 1024 neurons. Finally, these 1024 neurons are connected to 2 output neurons, representing the classes of \"defective\" or \"non-defective\" and softmax function is applied to get the network's probability distributions for the given input pair. For the feature extraction part of the architecture, ResNet models pretrained on the ImageNet [28] dataset are used as this approach has been demonstrated to result in faster convergence during training [29]. Pretrained ResNet architectures come in many depths but we chose the 34-layer one in an effort to find balance between performance and inference speed, as the defect detection part of the PDDS tends to be the bottleneck in terms of speed. A condensed scheme of the described two-stream classification network can be seen in Figure 7.
###### Abstract  
A breakthrough in the technology for virtualizing satellite-borne networks and computing and storage resources can significantly increase the processing capacity and resource utilization efficiency of satellite-borne base stations in response to the development trend in multi-star and multi-system converged satellite internet iterative systems. The protocol processing function of traditional satellite communication systems is generally placed in the ground station system for processing, with poor flexibility and low efficiency. As a result, a reconfigurable digital satellite-borne base station architecture design is suggested, allowing for separation of the hardware and software of the satellite-borne base station and flexible programming and dynamic loading of the satellite-borne base station's functions by software. Meanwhile, a fast adaptive migration algorithm based on multi-dimensional environment awareness is proposed on top of the reconfigurable digital base station, and migration precomputation and real-time computation are added in order to realize rapid deployment of the digital base station system network. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing system stability and decreasing real-time calculation costs associated with system network migration under conditions of high dynamic changes for each network element in a star-loaded environment. In conclusion, a digital satellite-borne base station system that effectively addresses the issues of low flexibility and high dynamic changes of nodes in the resource-constrained satellite environment can be created by combining the adaptive migration algorithm and the reconfigurable digitized satellite-borne base station architecture.  
virtual function; fast migration algorithm; satellite-borne
## 3 Controller Design and Stability Analysis
### PID Position and Altitude Control Design  
\(P_{z}\) is the PID controller of UAV altitude in the \(z\) direction, which is defined as  
\[P_{z}=K_{zP}\left(z_{d}-z\right)+K_{zI}\sum_{i=1}^{k}t_{s}\times\left(z_{di}-z _{i}\right)+K_{zD}\left(z_{d}-z\right), \tag{2}\]  
where \(K_{zP}\), \(K_{zI}\), and \(K_{zD}\) are the proportional, integral, and differential coefficients in the controller, respectively, and \(t_{s}\) is defined as the time step.  
Figure 4: The proposed soft landing gear.  
Figure 5: The control block diagram of the proposed approach.  
\(P_{x}\) and \(P_{y}\) are the PID controller of the UAV position in the x-y plane, which are defined as  
\[\begin{array}{l}P_{x}=K_{xP}\left(x_{d}-x\right)+K_{xl}\sum_{i=1}^{k}t_{s} \times\left(x_{di}-x_{i}\right)+K_{xD}\left(\hat{x}_{d}-\hat{x}\right)\\ P_{y}=K_{yP}\left(y_{d}-y\right)+K_{yI}\sum_{i=1}^{k}t_{s}\times\left(y_{di}-y_{ i}\right)+K_{yD}\left(\hat{y}_{d}-\hat{y}\right).\end{array} \tag{3}\]  
The desired attitude angles (roll and pitch) can be derived as  
\[\begin{array}{l}\phi_{d}=-\arctan\left(\frac{P_{y}}{P_{x}+g-D_{x}}\right), \\ \theta_{d}=\arctan\left(\frac{P_{x}}{P_{x}+g-D_{x}}\right).\end{array} \tag{4}\]
# An Intelligent Approach for Contact Pressure Optimization of PEM Fuel Cell Gas Diffusion Layers
## 4 Results and Discussion
### Agent Model Performance  
Bolt position and clamping force play big roles in the contact pressure of the GDL, so these two factors are considered design variables. In Figure 9, \(L_{1}\), \(L_{2}\), and \(L_{3}\) are positions of the three bolts, and Bolt 4 is fixed because it is right on the symmetric plane, so a change in position of Bolt 4 will lead to an increase in bolt numbers that is not preferable. So, design variables are the positions of Bolts 1-3 and the clamping forces on Bolts 1-4. The position of the bolt can only slide along the edge to ensure the reaction area is not affected. As a result, an 80-run design space is generated using the Opt LHD method.  
Figure 8: Contact pressure distributions (**a**) along the channel and (**b**) across the channel.  
Eighty simulations were run by the FE model, and corresponding contact pressure results of the GDL were obtained. Thirty observation points (see Figure 9) were picked averagely on the GDL, and the data space was conducted with the contact pressure on the GDL. Seventy-five out of eighty datasets were randomly picked as training data, and the remaining 5 groups (noted as R2, R6, R15, R62, R76) were taken as test data, as listed in Table 2. An RBFNN-based agent model was then trained and tested. Relative error (RE) is employed to assess the performance of the agent model. As shown in Figure 10, the average RE between predictions of the agent model and the FE results is 4.9%. Figure 11 shows the error contour of contact pressure on the GDL obtained from the observation points. It can be seen that the REs of most areas are lower than 5%, and the biggest gap between the prediction and the FE model is around 7.5%. Nevertheless, the prediction accuracy of the RBFNN-based agent model is more than acceptable, considering the computational cost is significantly decreased.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Run** & \(\mathbf{F_{1}}\) & \(\mathbf{F_{2}}\) & \(\mathbf{F_{3}}\) & \(\mathbf{F_{4}}\) & \(\mathbf{L_{1}}\) & \(\mathbf{L_{2}}\) & \(\mathbf{L_{3}}\) \\ \hline R1 & 76.9 & 2076.9 & 1923.1 & 2769.2 & 101.2 & 68.9 & 16.5 \\ R2 & 1153.8 & 1769.2 & 76.9 & 1692.3 & 120.3 & 42.4 & 36 \\ R3 & 1692.3 & 1307.7 & 1230.8 & 2615.4 & 123.7 & 72.4 & \(-\)17 \\... &... &... &... &... &... &... &... \\ R80 & 1307.7 & 615.4 & 2076.9 & 2461.5 & 92.1 & 52.8 & 35 \\ \hline \end{tabular}
\end{table}
Table 2: Train and test data sets generated by the optimal Latin hypercube design (Opt LHD).  
Figure 10: Predictions of the contact pressure of the agent model and the FE model.  
Figure 9: Schematic of the structure design of fuel cell packing.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 5 Numerical Results
### LT Parameters  
This subsection optimizes LT codes' parameters (i.e., \(c\) and \(\delta\)) depending on the optimizing model, Equation (9), shown in Section 4.2. The simulation results are run over 10,000 transmissions in each scheme to obtain averages of the implementation measurements. Furthermore, all three channels in the Y-network are assumed to have an erasure probability of \(\varepsilon_{1}=\varepsilon_{2}=\varepsilon_{3}=0.01\).  
Figure 4 shows the DSP of LT codes versus the overhead (\(\epsilon\)) at \(k=128\) for different values of \(c=\{0.01,0.02,0.05,0.1,0.2,0.5\}\) and \(\delta=0.05\) (Figure 3(a)), \(\delta=0.1\) (Figure 3(b)), \(\delta=0.2\) (Figure 3(c)), and \(\delta=0.5\) (Figure 3(d)). As a matter of fact, the best \(c\) and \(\delta\) values depend on the code length \(k\) and the overhead, \(\epsilon\) (or, equivalently, the number of received symbols \(n\)). However, as the value of \(c\) increases, the performance of DSP will be decreased. Thus, it approaches 100% slower. Therefore, at packet length \(k=128\) and overhead \(\epsilon>0.2\), the best value of \(c\) is around 0.02, and the best value of \(\delta\) is around 0.5.  
Figure 4: Decoding success probability (DSP) versus the overhead (\(\epsilon\)) for LT codes of \(k=128\) at different values of \(c\) and \(\delta\). (**a**) \(\delta=0.05\). (**b**) \(\delta=0.1\). (**c**) \(\delta=0.2\). (**d**) \(\delta=0.5\).  
Likewise, Figure 5 shows the DSP of LT codes versus the overhead (\(\epsilon\)) but at \(k=256\). In general, the performance at \(k=256\) is better than the performance at \(k=128\). Moreover, the same observations as in the case of \(k=128\) are obtained. As the value of \(c\) increases, the DSP performance becomes worse. Similarly, the best value of \(c\) is around 0.02, and the best value of \(\delta\) is around 0.5.  
Fundamentally, the \(\delta\) parameter shows the failure probability of the decoder after it receives \(n\) encoded symbols [9]. In addition, the \(\delta\) value shows how much the generator matrix is sparse. Thus, as \(\delta\) increases, the average degree of the encoded symbols is decreased. Therefore, there is a trade-off between the failure probability and the average degree of the encoded symbols (i.e., complexity). Further, the \(c\) parameter is a positive constant that highly affects the performance of the code. Thus, if \(c\) increases, the probability of degree-one encoded symbols is increased (which is very important to start the decoding process). However, simultaneously, the average degree of the encoded symbols and the complexity increase. In summary, we choose \(c=0.02\) and \(\delta=0.5\) as the optimal values in terms of the DSP in the rest of our work.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 3 Methodology
### Complex Trajectory Gesture Recognition
#### 3.2.4 Neural Network for Comparison  
To verify the gesture recognition performance of the proposed network, a three-channel convolutional neural network was selected and compared. The effectiveness and robustness of the proposed neural network in recognizing gesture actions were evaluated based on comparison results across various parameters. The schematic diagram of the structure of the three-channel convolutional neural network is shown in Figure 16.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Layer Size** & **Output Size** \\ \hline Input layer & \(16\times 16\times 16\times 1\) & \(16\times 16\times 16\times 1\) \\ \hline \multicolumn{3}{c}{3D CNN} \\ \hline Conv1 + ReLU & \(5\times 5\times 5\) & \(16\times 16\times 16\times 10\) \\ Maxpooing1 & \(2\times 2\times 2\) & \(8\times 8\times 8\times 10\) \\ Conv2 + ReLU & \(3\times 3\times 3\) & \(8\times 8\times 8\times 10\) \\ Maxpooling2 & \(2\times 2\times 2\) & \(4\times 4\times 4\times 10\) \\ FC1 & 256 & \(1\times 1\times 1\times 256\) \\ Dropout + ReLU & 0.5 & \(1\times 1\times 1\times 128\) \\ FC2 & 64 & \(1\times 1\times 1\times 64\) \\ Dropout + ReLU & 0.5 & \(1\times 1\times 1\times 32\) \\ Flatten & \(\backslash\) & 32 \\ \hline \hline \multicolumn{3}{c}{Bidirectional LSTM neural network} \\ \hline Bi-LSTM & 256 & 256 \\ FC3 & 32 & 32 \\ Dropout & 0.5 & 16 \\ FC4 & 4 & 4 \\ Softmax & \(\backslash\) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bidirectional LSTM fusion neural network parameters.  
Figure 16: Schematic diagram of three-channel convolutional neural network.  
The input data for each channel of the three-channel convolutional neural network are two-dimensional image data, which include the distance time map, azimuth time map, and elevation time map of gesture. Each channel contains a two-layer convolutional pooling structure. Due to the mean normalization of the input data, the input data size is 32 \(\times\) 32. Therefore, the convolution kernel size and hyperparameter settings for each channel are the same for reducing network complexity. After completing the high-dimensional feature extraction of the data between the three channels, a self-attention layer is introduced to fuse the three-channel data. By calculating the similarity and Softmax processing of the outputs of the three channels, the attention spectrum between the channels can be obtained. Afterwards, the attention spectrum is fused with the initial gesture features to obtain dependency information between the three channels. Finally, the prediction results of the classification are output through two FC layers to achieve the classification and recognition of gestures.  
The input data size of the three-channel neural network is 32 \(\times\) 32, and the parameter settings of the convolutional neural network and fusion classification module are shown in Table 4.
This paper will also present an effective analysis of the key enabling architectures, main applications, challenges, and opportunities for ELEs and healthcare systems. While several survey papers regarding IoT for the healthcare domain are available in the literature and the recent proliferation of IoT platforms is evident, it can also be observed that these surveys do not focus on the examination of existing IoT platforms and operating systems. Therefore, the key novelty of this paper is that it performs a comprehensive and comparative study of the available IoT platforms and operating systems and recommends one of them to specifically address solutions for the healthcare domain. Furthermore, this document synthesizes the existing body of knowledge and identifies common threads and gaps that open up new significant and challenging future research directions for healthcare systems. Taking into account the importance of security, privacy, and quality of service (QoS) open issues in the healthcare field, the paper highlights various insights surrounding healthcare systems. The discussion on numerous key future research topics, with the potential to accelerate the progress and deployment of IoT in healthcare, is expected to provide an important background for future research initiatives. In conclusion, this review article aims to be useful by introducing the topic to not only academics or engineers but also o healthcare professionals, which is essential for the development of future healthcare systems. The rest of this paper is structured as follows: This paragraph ends Section 1; Section 2 focus on IoT visions, elements, open-source platforms, smartphones, and wearable applications; Section 3 refers to IoT applications in healthcare, and Section 4 focuses on important open issues, such as QoS, security, availability, and interoperability; Section 5 discusses several key future research topics with the potential to accelerate the progress and deployment of IoT in healthcare systems; and Section 6 concludes the paper.
## 3 Methodology
### Overall Framework  
The framework is mainly composed of a text detection network and a background inpainting network. Specifically, the text detection network consists of an Integrated Differentiating Feature Pyramid and a Texture Fusion Module, while the background repair network comprises a Segment Anything Model (SAM) and a Latent Diffusion Model (LDM) [17]. Although the SAM possesses powerful capabilities for image segmentation, it relies on manually provided input prompts, which limits the efficiency and scalability of the segmentation process. To address this issue, this paper introduces an Integrated Differentiating Feature Pyramid and a Texture Fusion Module prior to the SAM module. Both modules independently generate input prompts, aiming to combine their segmentation abilities with those of SAM to enhance the accuracy and efficiency of segmentation while reducing reliance on manual labor costs. The mask generated via the SAM module serves as the conditional input for the latent diffusion model within the background repair network, guiding its reverse diffusion process.  
The overall workflow of MTF-STTR is illustrated in Figure 2. Initially, the input image is fed into the backbone network of the Integrated Differentiating Feature Pyramid to extract multi-scale features, which are then used to generate feature representations rich in contextual information. With inspiration from the DBNet++ [18] method, probability maps and threshold maps are predicted based on these feature representations. By combining the probability map with the feature representation, an approximate binary map is calculated to locate the text area. The boundary box generation mechanism allows for the easy extraction of the boundary boxes directly from the approximate binary map. These boundary boxes are then used as prompt inputs to the segmentation model to generate masks, thus minimizing the need for manual annotation. Subsequently, the latent diffusion model is applied for text erasure and image repair, culminating in the generation of an image with erased text.  
Figure 2: Architecture of the proposed MTF-STTR network.
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 5 Experiment Design
### Evaluation Metrics  
To compare the correctness of the proposed segmentation methods with the manual annotations, we employ the following evaluation metrics:  
1. Dice Coefficient--A measure of overlap between the segmented region in the ground truth and prediction. It is calculated for each of the segmented classes (Vitreous,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Training** & **Validation** & **Testing** \\ \hline All images & 5608 & 721 & 721 \\ All images \(-\)3\% anomalies & 5434 & 701 & 703 \\ All images \(-\)3\% anomalies + 4\(\times\) data augm. & 27,170 & 701 & 703 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of images used for the training and evaluation.  
Figure 5: Circular distributions of retina orientations in each subset of the CAVRI dataset.  
Figure 6: Distribution of center of mass of images. Blue dots represent calculated position of center of mass for each OCT cross-section. Red ‘+’ sign denotes the mean center of mass, and the red ellipsis encapsulates the 2.5 standard deviation of the data. The data is presented on the plane with resolution equal to those of the OCT cross-sections.  
Preretinal Space, Retina, Region under Retina) based on Equation (5) separately and averaged over all test samples. Values closer to 1 represent better performance. The standard deviation (SD) values illustrate scores distribution.
2. Mean Absolute Error (MAE) with standard deviation (SD)--The average vertical distance between an annotated line and a segmented boundary. It represents the error of the segmentation result relative to the ground truth. It is calculated as follows: \[MAE_{z,b}=\frac{1}{w}\sum_{x=1}^{w}|P_{z,b}(x)-G_{z,b}(x)|,\] (9) where \(P(x)\) and \(G(x)\) are the vertical position of the class boundary line \(b\in B\) for the prediction and ground-truth, respectively; \(x\) stands for the image column index within the image width \(w\); and \(z\) denotes an individual image index. The MAE is computed for the three segmentation lines of interest, namely the \(B=\{0:PCV,1:ILM,2:RPE\}\), and is averaged over the number of all B-scans used in the test.
3. Topology Incorrectness Index (TII)--that indicates what percentage of tested images has incorrect layers topology in the vertical direction. It is computed based on a vertical gradient of the predicted segmentation mask.