esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of IoT-Based Health and Environment WSN Deployment
## 3 Health and Environment Sensor Network Deployment
### GIS-Based WSN Deployment
#### 3.1.3 Routing Algorithm  
To manage the energy power consumption for maximizing the lifetime of the network, a routing protocol is required to consider in the designed network [5; 80]. There are many different routing protocols, however, Minimum Spanning Tree (MST) is one of them to optimize the energy consumption by minimizing the total transmission [82]. MST is a tree where any pair of vertices are connected by exactly one path and is a unique spanning tree with a minimum total edge length [71]. Minimum spanning tree is a weighted subgraph of spanning tree which is a subgraph of an undirected and connected graph without loops [83]. In general, a tree is a spanning subgraph of unit distance graph (UDG). The UDG is formed by creating edges to connect all nodes that are within the communication range (called unit distance), \(R_{c}\). UDG can formally be defined as [71]:  
\[\mathrm{UDG}=\left(\mathrm{V},\ \left\{(\mathrm{u},\mathrm{v})\right\}\right| \mathrm{(u},\mathrm{v})\in\mathrm{E\ and\ 0<\delta(\mathrm{p(u),\ p(v))<R_{c}}}\right) \tag{10}\]  
\[\delta(\mathrm{p(u),\ p(v))=\ \sqrt{\Delta\mathrm{X_{u,v}}^{2}+\Delta\mathrm{Y_{ u,v}}^{2}}} \tag{11}\]  
where \(p\) is the position of sensor nodes in \(\mathbb{R}^{2}\). MST that connects all \(n\) sensor nodes with \(n-1\) edges has minimum total weights of edges [71; 80]. In MST, the length of each of the edges can be considered as its weight; in this case, the sum of the length of MST edges is minimum. There are two efficient algorithms to construct MST including Kruskal [84] and Prim [85] algorithms.
## 2 Results
### PtFPPS1 Produces FPP While PtGPPS1.LSU and PtGPPS2.LSU Have GPPS and GGPPS Activity In Vitro  
The full-length open reading frame (ORF) of PtFPPS1 and the N-terminal truncated ORFs of PtGPPS1.LSU (\(\Delta\)71), PtGPPS2.LSU (\(\Delta\)72), PtGPPS.SSU1 (\(\Delta\)41), and PtGPPS.SSU2 (\(\Delta\)45) lacking the predicted signal peptide sequences were cloned into a bacterial expression vector and heterologously expressed in _Escherichia coli_. Purified recombinant proteins were incubated with the potential substrates IPP and DMAPP and product formation was analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The enzyme assays revealed that PtFPPS1 catalyzes the condensation of IPP and DMAPP to FPP and traces of GGPP (Figure 3). PtGPPS1.LSU and PtGPPS2.LSU, however, produced both GPP and GGPP, but no FPP. Under the conditions in our assays, the GPPS activity of PtGPPS1.LSU was higher in comparison to its GGPPS activity, while PtGPPS2.LSU showed the opposite behavior and produced more GGPP than GPP (Figure 4). The two GPPS small subunits PtGPPS.SSU1 and PtGPPS.SSU2 had no activity when assayed with IPP and DMAPP (Figure S4).  
Figure 3: IDS activity of PtFPPS1, PtGPPS1.LSU, and PtGPPS2.LSU. The genes were heterologously expressed in _Escherichia coli_ and purified recombinant proteins were incubated with the substrates IPP and DMAPP. Products were analyzed using LC-MS/MS and identified with authentic standards. The empty expression vector was expressed under the same conditions, assayed with IPP and DMAPP, and used as negative control.
# FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo
## 4 Discussion
### Effectiveness of the New Regularization Constraints  
Most of the recent studies use the normal vector solved by the reflected illumination model as the optimization target, but the solution from the normal vector to the depth is also a complex problem. To verify the effectiveness of our new regularization constraints, we used the proposed network and the same configuration, but used a common loss function with a general L2 norm to train the dataset, which was used in the recent related work [27]. By comparing group (f) and group (g) of these three samples in Figure 5, we can find that ours(g) contained more details than the method with the general L2 norm did (f). Since our optimized target also included the original image of the object, generating the input images could help our network to correct the prediction of the depth, so that the reconstruction result was closer to the real. Thus, ours was clearer for the reconstruction of the simple sample surface (corrugates), and the transition of the cracks on the defects was also smoother (Sample 3). As shown in Figure 5 (Sample 2, Sample 3), there was an abnormal bulge around the defects as we can see in group (f), but from the original image and GT corresponding to the sample, this did not conform to the real situation. However, ours had a good effect on the optimization of this special position--that is, the transition from plane to defect was more reasonable. In addition, Table 1 shows the quantitative analysis results of our method and the general L2 norm. It can be seen from the table that our method significantly improved on the parameters rms and rel. However, the threshold accuracy of ours was slightly lower than that of the L2 norm. The main reason for this, we think, was that the restoration of the images made our reconstruction results closer to reality rather than only taking the depth GT as the optimization standard. Therefore, the accuracy of the depth prediction was lower than that of GT, but it could also get the same level of L2 loss within a certain accuracy range.  
Figure 5: Examples of the results of our method and others. The samples in the example are all from the test set: (**a**) the first column shows the basic 2D information of this sample; (**b**) the corresponding ground truth data (calculated using linear structured light scan) are shown in the second column; (**c**–**e**) the third to fifth columns demonstrate the classic multi-source Photometric Stereo approach using 5,10, and 20 input images; (**f**) the sixth column shows the results using a general L2 loss function; (**g**) the result estimated by our network is shown in final column.
## 3 Simulation Experiments and Statistical Analyses of Results
### Comparison of IGGIII-BFGS Algorithm with BFGS Algorithm  
The gross error rate in the observed data is generally not more than 10%. Therefore, based on the true values, random errors with a median deviation of 10 mm were added, and the gross error of 200 mm is added to one to two maximum subsidence values, edges, and near the inflection points in each experiment as the new test data. To reduce randomness in the experimental results, 20 inversions of the BFGS algorithm and IGGIII-BFGS algorithm were performed for each experiment, and the mean values of the parameters and their relative errors of the inversions of the two algorithms are given in Table 1.  
Figure 5: Relative errors of inversion parameters under different robust estimation methods. Table 1 shows that when the number of roughness is one, the maximum relative errors of the three main parameters q, b, and tan \(\beta\) using BFGS inversion are 2.13%, 1.90%, and 1.35%, respectively; when the number of roughness is two, the maximum relative errors of the three main parameters q, b, and tan \(\beta\) using BFGS inversion are 4.11%, 3.60%, and 2.30%, respectively; and the maximum relative error of the three main parameters q, b, and tan \(\beta\) using IGGIII-BFGS inversion for the three parameters is 0.00% in all cases. For the four parameters s\({}_{1}\), s\({}_{2}\), s\({}_{3}\), and s\({}_{4}\), the results of the inversion by the two methods are not much different in general. It can also be seen from Table 1 that the effect on the inversion accuracy of parameters q and b is greatest when the roughness appears near the maximum subsidence value, and the effect on the inversion accuracy of parameters q and b is smallest when the roughness appears at the edge. The effect on the inversion accuracy of tan \(\beta\) is greatest when the roughness appears near the inflection point, and the effect on tan \(\beta\) is smallest when the roughness appears at the edge. Figure 6 shows the comparison of subsidence value and horizontal movement value. Two points are selected near the maximum subsidence value to join the 200 mm roughness, respectively, using two methods of parameter inversion experiments to obtain the parameter mean value, using the parameter mean value of the expected subsidence value of the plotted curve and then compared with the measured value. It can be seen that the curves predicted by the IGGIII-BFGS method nearly coincide with the observed values, which is better relative to the BFGS method. The absolute value of the error between the subsidence value and the horizontal movement value predicted by the IGGIII-BFGS algorithm and the true value are all within 10 mm, the absolute value of the error of BFGS algorithm exceeds 10 mm in many points, and some points even exceed 90 mm. The root mean square error (RMSE) is the square root of the ratio of the sum of the squares of the deviations of the predicted values from the observed values to the ratio of the number of observed points,
## 2 Gaussian Processes
### Covariance Functions  
There exists a large variety of kernel functions, which have been extensively studied and reviewed in [36]. The de facto default kernel used in Gaussian processes is the squared exponential kernel, also called the radial basis function kernel, the Gaussian kernel, or exponentiated quadratic kernel. It is applicable in a wide variety of situations because it is infinitely differentiable and thus yields smooth functions. Besides that, it only takes two hyperparameters to tune. It has the form:  
\[k_{SE}(\mathbf{x},\mathbf{x}^{\prime})=\sigma_{f}^{2}\exp\left(-\frac{| \mathbf{x}-\mathbf{x}^{\prime}|^{2}}{2l^{2}}\right) \tag{8}\]  
in which \(\sigma_{f}^{2}\) is a height-scale factor and \(l\) the length-scale that determines the radius of influence of the training points. For the squared exponential kernel, the hyperparameters \(\boldsymbol{\theta}_{SE}\) are \(\left\{\sigma_{f}^{2},l\right\}\). For the function \(k:X\times X\to\mathbb{R}\) to be a valid kernel, it must be positive semi-definite (PSD), which means that, for any vector \(\mathbf{x}\in X^{d}\), the kernel matrix \(K\) is positive semi-definite. This implies that \(\mathbf{x}^{T}K\mathbf{x}\geq 0\) for all \(\mathbf{x}\in\mathbb{R}^{d}\). In this work, we only use the squared exponential kernel.
# Failure-Robot Path Complementation for Robot Swarm Mission Planning
## 3 Design and Principle of Path Programming
### A\({}^{*}\) Search Algorithm  
The evaluation function \(f(n)\) is the core of the A\({}^{*}\) Search algorithm. As shown in function (12), it is the aggregation of \(g(n)\) and \(h(n)\); \(g(n)\) is the travel distance from the start point to any specified node \(n\), and \(h(n)\) refers to the linear distance from any node \(n\) to the target point.  
\[f(n)=g(n)+h(n) \tag{12}\]  
The algorithm provides a better evaluation function, which is capable of efficiently determining the optimal path between the start point and the end point; therefore, it was used to program paths bypassing the no-travel zone.
# An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels
## 2 Related Works  
### Chest X-ray Image Classification with Noisy Labels  
Chest X-ray (CXR) image classification with deep learning has been widely explored and achieved significant progress in recent years. Some methods focus on generating more discriminative features to facilitate classification by designing novel architectures in deep neural networks [4; 5; 6; 7; 8; 9; 10]. Some other methods use the attention mechanism to improve the classification performance by focusing on the lesion area, e.g., [11; 12; 13; 14; 15; 16]. In addition, some methods try to improve classification performance by capturing dependencies between chest disease labels [17; 18; 19].  
Label noise is inevitably introduced into the large-scale chest X-ray image dataset due to the errors produced by the annotators or the annotation algorithms. The deep networks are easily overfitted to these noisy samples, which leads to performance degradation. To combat the noise in medical images, [PERSON] et al. [20] investigate the probabilistic methods against label noise in solving medical image analysis tasks. [PERSON] et al. [21] use the label smoothing regularization (LSR) technique [22] to handle noisy samples. [PERSON] et al. [23] propose to use model confidence and uncertainty as metrics to identify samples mislabeled as emphysema in the chest X-ray images. [PERSON] et al. [24] propose to correct the label noise by building an attribute-level graph and estimating the virtual attributes of CXR images. [PERSON] et al. [25] propose a Bootstrap Knowledge Distillation (BKD) method to correct noisy labels and theoretically deduce that the distribution of distilled labels is closer to the ground truth. [PERSON] et al. [26] use an improved Early Learning Regularization (ELR) [27] loss function to robustly combat noise. [PERSON] et al. [28] design a noise-resistant loss function that takes the human-assessed label noise probability and the observed label correlation as two priors for the loss functions. [PERSON] et al. [29] propose a collaborative training framework to filter and train clean samples. Some methods also use a self-supervised learning strategy for noisy samples and design a loss function to mitigate overfitting to noisy labels in global and local representation learning for CXR images.  
Most of the existing methods aim at designing robust loss functions or designing efficient strategies to identify the noise labels and then correct them. Unlike previous works, we consider the label noise from the aspect of label generation and design a robust model to combat noise for chest X-ray image classification.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 5 Applications  
### Applications in Coupling Trains  
The existing coupling communication is mainly divided into two types. The communication between carriages is referred to as coupling, while the connectivity between trains is referred to as virtual coupling. The virtual coupling train has a longer communication distance than the coupling train. The above conclusion can be applied to the coupling trains of 2 \(\times\) 2 and 3 \(\times\) 3 antenna arrays for wireless communication, as shown in Figures 10 and 11.  
Figure 9: Channel capacity under two different 3 \(\times\) 3 antenna configurations: (**a**) circular arc antenna array; (**b**) rectangular antenna array.
## 4 Discussion
### Meteorological Factors  
The natural factors that affected the variations in water quality were mainly precipitation and wind speed. Figure 16. shows the PCC results of 41 lakes and reservoirs in Guangzhou and Shenzhen. Two lakes and reservoirs in Guangzhou were positively correlated with precipitation, while the rest were negatively correlated. Furthermore, eight lakes and reservoirs in Shenzhen were positively correlated with precipitation, while the rest were negatively correlated. Precipitation increases water volume: when the water volume increases, the concentrations of SPM, chlorophyll, and other substances are diluted, thereby making the water clearer and reducing its FUI. However, precipitation also affects the runoff volume, which affects the concentrations of organic and non-polar suspended solids in the water, as well as the aquatic vegetation transported into lakes and reservoirs, thereby making the water turbid and increasing its FUI.  
Figure 15: Inter-annual FUI variations in Shenzhen.  
Wind speed was the main factor affecting the water quality of the inland lakes and reservoirs. When the wind speed increases, it causes substances in the water to become resuspended, thereby making the water turbid. Strong winds also transport some pollutants from the shore into the water, thereby producing increased turbidity.  
As both Guangzhou and Shenzhen have a subtropical climate, the temperature remains high all year, and many typhoons occur annually. Affected by the temperature, nutrients accumulate in lakes and reservoirs, and the amount of floating algae in the water increases, leading to increased turbidity of the water body, which increases the FUI. Figure 16 shows the numbers of typhoon landing days in the study area. Whenever a typhoon makes landfall, the wind speed and power increase. Under these conditions, strong winds disturb sediments in lake and reservoirs, causing the sediment to re-suspend, leading to higher turbidity and an increase in the FUI.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Indoor Environments with Single High Frequency Acoustic Waves
#### 5.2.2 Affected by the Azimuth Angle  
We plot the mean angular errors on different distances \(L\) = 5 m, 10 m, 20 m, 30 m, \(\beta=\theta=0^{\circ}\) and azimuths \(\phi=30^{\circ},90^{\circ},240^{\circ}\) between the smartphone and the speaker using both VULA and VUCA in Figure 9. Then, through the observation, there are not many differences between the direction finding performance of the VULA system and the VUCA system at the same distance between the acoustic source and the smartphone when \(\phi=90^{\circ}\). However, when \(\phi=30^{\circ}\), the VUCA obviously performs better than VULA.  
We believe the reasons for this phenomenon are the fact that the VULA system's resolution to the acoustic signal is changing, and it is related to the rate of change of the direction vector. The resolution reaches the highest level when the directions of the arrived signal are perpendicular to the axial line. When the signal comes close to the same direction along the axial line of VULA, the resolution gets lower and lower. However, the VUCA system's resolution remains the same for \(360^{\circ}\) duration.  
Eventually, when \(\phi=240^{\circ}\), the VULA is extremely unstable. In practice, we can not make sure that the acoustic source is right in front of us. Thus, we cannot afford the possible serious errors of the VULA system. This is why we choose to use the VUCA system.  
Figure 8: Cumulative distribution function (CDF) of the angular errors when using VUCA and \(L\) = 5 m, 10 m, 20 m, 30 m.
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 4 Simulation and Numerical Results
### Computational Complexity Analysis  
The complexity of the BCD is \(O(I_{o}(2 NMK+KM^{2}+K^{2}M^{2}))\)[14], where \(I_{o}\) is the number of iterations and does not include the complexity of the channel estimation. For IRSs with passive elements, the conventional least square channel estimation methods have a computational complexity of \(O(LKMN)\) with \(L\) < _NM_. In our proposed RBF-GAT method, the channel estimation is omitted. For the training stage, let \(Z_{1}\), \(Z_{2}\), and \(Z_{3}\) denote the number of neurons in the three FC layers in turn. The computational complexity of the proposed RBF-GAT scheme in each iteration is \(O(KFF^{\prime}+KF^{\prime\prime}(2 ML+F^{\prime})+F^{\prime\prime}Z_{1}+Z_{1}Z _{2}+Z_{2}Z_{3}+2Z_{3}MK+2Z_{3}N)\). In the training phase, the model is trained for \(E_{p}\) epochs, with each epoch being \(I_{ite}\) iterations. Hence, the total computational complexity of the proposed method is \(O(E_{p}I_{ite}(KFF^{\prime}+KF^{\prime\prime}(2 ML+F^{\prime})+F^{\prime\prime} Z_{1}+Z_{1}Z_{2}+Z_{2}Z_{3}+2Z_{3}MK+2Z_{3}N))\). The high computational complexity training process is performed offline. Therefore, the actual computational complexity of our proposed method is only linear in \(M\), \(N\) and \(K\).  
Due to the GNN and MLP methods are also established by neural networks, and they have approximate computational complexity as the proposed RBF-GAT. However, the proposed method achieves better performance, which is shown in the previous subsection. It is easy to see that the proposed RBF-GAT method has lower computational complexity and has significant advantages in the dynamic scenarios of satellite communication.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 2 Materials and Methods
### Acquisition Strategy  
According to the range of values of the carrier-to-noise ratio set in Table 3, we generated signals with different \(C/N_{0}\) in steps of 1 dB-Hz. Furthermore, with the same \(C/N_{0}\), we performed 100 random tests for each algorithm scheme by Monte Carlo, i.e., the signal contains randomly different pseudo-random noise (PRN) satellites with a random Doppler shift and code phase. Considering that coherent integration alone still has difficulty in acquiring the signal at super low carrier-to-noise ratios, we used the coherent integration algorithm in combination with other algorithms. Therefore, in semi-bit coherent integration (SBCODI) and SBCODI, we accumulated a segment of signals spaced 10 ms apart to the desired integration length. The total integration time for each method was set to 0.8 or 1.6 s and the specific parameters are shown in Table 4.  
Figure 6: Block division by the alternate half-bit method, with each block containing 10 ms signals.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 2 Wireless Communication and Protocols' Characteristics
### Wireless Communication Security Aspects
There are five methods of security in the current Wi-Fi networks available to users. Below are the security measures from strongest to weakest: \begin{tabular}{l l} \(\odot\) & WPA2 + AES; \\ \(\odot\) & WPA + AES; \\ \(\odot\) & WPA + TKIP; \\ \(\odot\) & WEP; \\ \(\odot\) & Open Network (without security). \\ \end{tabular}   
Wi-Fi networks use the 4-way handshake process to establish the connection. The 4-way handshake is defined as a process of exchanging four messages between an access point (also know as authenticator) and the client device (also known as supplicant) in order to generate some encryption keys which can be used to encrypt actual data [24]. One of the most widespread threats in wireless technologies is a DoS (Denial of Service) attack that disrupts users' access to a network. This goal is usually achieved by overloading a given resource with a considerable amount of traffic or sending many malicious queries. Protection against it is almost impossible, but it is possible to find an intruder based on his signal [25]. Table 2 shows basic comparisions between three Wi-Fi networks security standards.