esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Method for Data Fusion and Evaluation Metrics  
Because the along-track ASCAT, HY-2B, and WindSAT products are available in ascending and descending directions, the spatial resolution of the fusion wind fields over global seas is 12 h. In the literature, the spatial interpolation approach has been applied for multiple remote-sensed product fusions. In this study, the following interpolation algorithm was employed, denoted as a Cressman interpolation:  
\[\overset{\rightarrow}{U}(\text{lat},\text{lon})\ =\ \overset{\rightarrow}{U}_{0}+ \frac{1}{n}\sum_{k=1}^{n}\frac{D_{k}^{2}-R^{2}}{D_{k}^{2}+R^{2}}\Delta \overset{\rightarrow}{U}_{10}, \tag{2}\]  
The spatial resolution of fusion wind was assumed to be a \(0.125^{\circ}\) grid. The advantage of this method is that the interpolation result can be adjusted by distance under the condition of ensuring accuracy, indicating that the spatial resolution of fusion wind can be artificially selected. In order to ensure that there were available data on all grids, the radius, \(R\), was taken as 50 km, and there were \(n\)-element-referred wind vectors (\(\overset{\rightarrow}{U}_{k}\)) from the scatterometers (ASCATs, HY-2B, and WindSAT) in both latitude and longitude directions. \(\overset{\rightarrow}{U}_{0}\) is the initial ASCAT or HY-2B scatterometer wind vector nearest the grids, \(D_{k}\) is the spatial distance, measured in km, between the individual, referred and initial winds, \(\Delta\overset{\rightarrow}{U}_{10}(=\overset{\rightarrow}{U}_{0}- \overset{\rightarrow}{U}_{k})\) is the difference between the individual referred and initial wind vectors, and \(\overset{\rightarrow}{U}(\text{lat},\text{lon})\) represents the fusion wind vector at a gird of the latitude and longitude. We note that the WindSAT winds were only used at wind speeds smaller than 25 m/s, as applied above the interpolation, due to the winds from scatterometers suffering the saturation problem under greater wind-speed conditions. At wind speeds greater than 25 m/s, the measurements from WindSAT were directly used.  
Three metrics, i.e., the bias, the root mean square error (RMSE), and the correlation coefficient (COR), were chosen to investigate the performances of the fusion data. The calculation formulations used were as follows:  
\[\text{Bias}\ =\ \frac{1}{\eta}\sum_{k=1}^{n}X_{k}-Y_{k}\, \tag{3}\]\[\text{RMSE }=\sqrt{\frac{1}{n}\sum_{k=1}^{n}\left(X_{k}-Y_{k}\right)^{2}}\,, \tag{4}\]  
\[\text{COR }=\ \frac{\sum_{k=1}^{n}\left(X_{k}-\tilde{X}_{k}\right)\left(Y_{k}- \tilde{Y}_{k}\right)}{\sqrt{\sum_{k=1}^{n}\left(X_{k}-\tilde{X}_{k}\right)^{2} \sum_{k=1}^{n}\left(Y_{k}-\tilde{Y}_{k}\right)^{2}}}, \tag{5}\]  
where the matrices \(X\) and \(Y\) represent the evaluated and referred data, respectively; \(n\) is the sum of the samples; and \(\tilde{X}_{k}\) and \(\tilde{Y}_{k}\) represent the averages of the evaluated and referred data, respectively.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Coarse-to-Fine Keypoint Tracking
#### 3.2.2 Coarse-to-Fine Keypoint Tracking  
Having obtained the coarse relative pose in the initial stage, we proceed to match map points between adjacent frames and refine the pose of the current frame \(\xi_{w}^{c}\) through the 3D-2D projection relationship. Subsequently, leveraging the coarse relative pose \(\xi_{lc}^{\text{coarse}}\), we project the 3D map points from the last frame's coordinate system to the pixel coordinate system of the current frame. Subsequently, we search for matching keypoints within a fixed radius range centered on the projection location, utilizing the Hamming distance of the descriptors as the search criterion.  
Upon establishing the 3D-2D matching relationships, we construct a pose graph. As expressed in Equation (7), we employ the coarse relative pose in conjunction with the last frame's pose to derive the initial pose of the current frame.  
\[\xi_{w}^{c}=\xi_{lc}^{\text{coarse}}\xi_{w}^{l} \tag{7}\]  
The current frame's pose serves as the vertex in the graph, with the number of vertices being subject to optimization. Map points from the current frame and the reprojection loss of these map points constitute the edges. We exclusively optimize the pose within the tracking module. As detailed in Equation (8), we iteratively refine the current frame pose \(\tilde{\xi}_{w}^{c}\) to minimize the reprojection loss. In our implementation, the g2o [48] framework is employed for optimizing this pose graph.  
\[\xi_{w}^{c}=\arg\min_{\tilde{\xi}_{w}^{c}}\frac{1}{2}\sum_{i\in\overline{\chi }}\
orm{u_{i}-\pi(\tilde{\xi}_{w}^{c}.\vec{p}_{i}^{w})}^{2} \tag{8}\]
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 4 Determining the Voltage Ratio and Measurement Uncertainty
### Measurement Uncertainty of Capacitance \(C_{2}\)  
The capacitance \(C_{2}\) consists of the capacitance of the probe \(C_{p}\) and the coaxial cable \(C_{c}\), as well as the input capacitance \(C_{s}\) of the analog input. The first two capacitances were measured together using an LCR meter, type ESCORT ELC-3131D, with the limit error described by the formula:  
\[E_{c}=\pm(0.006\cdot C_{MEAS}+5\cdot LSD) \tag{17}\]  
where \(C_{MEAS}\) is the measured capacitance value with the LCR meter and LSD is the least significant digit in the given measuring range. According to the data provided by the manufacturer of the DrDAQ card, the input capacitance \(C_{s}\) of the signal acquisition channel is 17 pF \(\pm\) 3 pF. After taking both these parameters into account and assuming a uniform distribution of errors, the standard uncertainty of the \(C_{2}\) capacitance measurement can be determined as:  
\[u(C_{2})=\sqrt{\left(\frac{E_{c}}{\sqrt{3}}\right)^{2}+\left(\frac{3\cdot 10^{ -12}}{\sqrt{3}}\right)^{2}} \tag{18}\]
# A Wideband Termination Based on Laser-Scribed Lossy Microstrip Line Structures
## 2 Structural Design
### Two-Port Configuration  
Two-port configuration is also considered to demonstrate the blockage of the transmission, as Figure 4 shows. The insertion loss \(|S_{21}|\) illustrates a significant drop from \(-18\) dB to over \(-50\) dB at 30 GHz with an increasing length \(l\) from 10 mm to 40 mm. On the other hand, all four cases show a similar low \(|S_{11}|\) level.  
The S-parameters as a function of the sheet resistance are plotted in Figure 5. The sheet resistances within the range from 20 \(\Omega/\square\) to 1.2 k\(\Omega/\square\) provide a sub\(-\)20 dB reflection level above 5.2 GHz. The \(|S_{21}|\) can change in a large range from a \(-15\) dB level to a \(-60\) dB level, accordingly. Therefore, the termination is possible for some attention applications by choosing a certain sheet resistance.  
Figure 2: (**a**) Calculated \(|S_{11}|\) of the laser-scribed tapered termination, rectangle termination, and the microstrip (MS) line. (**b**) Calculated \(|S_{11}|\) with a series of resistive film lengths.  
Figure 4: The S-parameters of the laser-scribed terminations with a series of rGO lengths \(l\) in two-port configurations.  
Figure 3: The S-parameters of the laser-scribed terminations with a series of sheet resistances of rGO film in the case of (**a**) an overlapped structure with copper strip conductors in the center, and (**b**) a design without copper strip conductors.
## 5 Satellite Remote Sensing Techniques for Infrastructure Monitoring  
Use of InSAR techniques allows the detection of displacements along the line of sight (LoS) (i.e., the observation direction) of the SAR sensor, and it consists in the measurement of the signal phase variation between images acquired on the same sections at different times. When a vertical displacement occurs on the ground, the distance between the sensor and the ground varies, affecting the phase of the signal back-received by the sensor (Figure 5).  
Figure 5: Side-looking satellite SAR data acquisition: (**a**) antenna illumination footprint; (**b**) sequence of acquisitions over the same area (master and slave images).  
An interferogram can be therefore generated from the phase difference between two SAR images, returning a matrix of numerical values comprised in the interval (\(-\pi\div\pi\)) and representing the surface movements. Thus, from the analysis of an interferogram, it is possible to recognise the scale of displacements on the area during the observation time. The two SAR images are named the master image and the slave image, respectively. The slave image must be co-registered and resampled with reference to the geometry of the master image. The interferometric phase is subsequently derived from a pixel-by-pixel multiplication with the conjugate complex of each pixel, in the master and slave image, yielding a value reported in Equation (1):  
\[\Phi^{w}\in[-\pi,\pi]\;[rad] \tag{1}\]  
where \(\Phi^{w}\) is the \"wrapped\" value of the interferometric phase.  
The phase \(\Phi\) is the key information in any interferometric application, as it relates to different phase components, as follows:  
\[\Phi=\Phi_{flat}+\Phi_{topo}+\Phi_{def}+\Phi_{atm}+\Phi_{noise} \tag{2}\]  
where \(\Phi_{flat}\), \(\Phi_{topo}\), \(\Phi_{def}\), \(\Phi_{atm}\) and \(\Phi_{noise}\) are the phase components for the reference surface, topography, deformations, atmospheric delay and noise, respectively. Displacements are detected by relating the signal phase variation between progressive acquisitions to the motion of the target under investigation (Figure 4). In detail, the general method consists in the comparison of a dataset of N SAR images, collected at different times on the same area of interest (AoI), and by calculating the interferometric phase \(\Delta\Phi\) for each N-1 pair, as referred to the master image. The component related to the ground deformations \(\Phi_{def}\) must be calculated from Equation (2), which is proportional to the sensor-to-target distance difference (\(\Delta\tau\)) divided by the wavelength (\(\lambda\)) of the SAR sensor, as expressed by Equation (3):  
\[\Delta\Phi_{def}=\frac{4\pi}{\lambda}\;\Delta\tau \tag{3}\]  
Multi-temporal InSAR (MT-InSAR) techniques were developed as an evolution of the DIfferential SAR Interferometry (DInSAR). Several techniques have been developed since, including persistent scatterers interferometry (PSI) and the permanent scatterers (PS-InSAR) techniques, with reference to the first algorithm presented by [PERSON] [14; 15] and the SBAS [16].
## 4 Error Modeling  
The error sources of the mechanism are as follows: (1) the deviation between the actual geometrical parameters and the original design values caused by manufacturing and installation; (2) clearance errors associated with revolute joints; (3) the driving error. At any time, there is the deviation \(\Delta\theta_{i1}\) between the actual rotation angle and the theoretical value from the actuator failing to drive the rotating arm \(A_{i}B_{i}\) to the desired angle. The deviation \(\Delta\theta_{i1}\) is called the driving error. (\(i=1\), \(2\), \(3\))  
By solving Equations (6) and (7), \(x\), \(y\) and \(z\) can be expressed as:  
\[\left\{\begin{array}{l}x=-c\sin\theta_{i2}\sin\alpha_{i}+r_{1}\cos\alpha_{i }-r_{2}\cos\alpha_{i}\\ +a\cos\theta_{i1}\cos\alpha_{i}+c\cos\theta_{i2}\cos\theta_{i3}\cos\alpha_{i} \\ y=-a\sin\theta_{i1}-c\sin\theta_{i2}\cos\theta_{i3}\\ z=-c\sin\theta_{i3}\cos\alpha_{i}-r_{1}\sin\alpha_{i}+r_{2}\sin\alpha_{i}\\ -a\cos\theta_{i1}\sin\alpha_{i}-c\cos\theta_{i2}\cos\theta_{i3}\sin\alpha_{i} \end{array}\right. \tag{11}\]
## 2 Related Works
### Checkpointing the Sink Node  
In [6], the authors proposed the concept of in-network fault tolerance for achieving enhanced network dependability and performance. In that scheme, the sink node periodically checkpoints its state and saves it in the memory of one or more sensor nodes, so called checkpoint sensors. When a sink node (\(S_{I}\)) fails or reaches an energy level below its threshold, another sensor node will be selected to operate as the new sink node (\(S_{2}\)). After applying this approach m times, the sink will be located in a sensor denoted by \(S_{m}\). If the sink is located on \(S_{m}\), then \(S_{m\cdot l}\) is the checkpoint sensor and the path between \(S_{I}\) and \(S_{m}\) is the checkpoint path. When a sink node (\(S_{m}\)) fails, \(S_{m\cdot l}\) detects the failure and becomes the sink instead; it iteratively operates in this sequence through the checkpoint path. This scheme is simple to implement, but energy consumption and reliability vary according to the position of the sink node.
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 4 Experimental Study
### Development of the ELM Model  
NumHLNs is related to whether ELM can achieve the expected prediction performance, and so it is necessary to investigate NumHLNs first to develop an optimal model. Two hundred ELM models with NumHLNs from 1 to 200 were developed, and each was run five times to predict the training subset and the validation subset (see Figure 8). As shown in Figure 8, 71 was chosen as NumHLNs after both considering the model performance and the thresholds of 5% between the overfitting and underfitting phenomenon, with an average \(\mathrm{Acc_{train}}\) of 0.9425 and an average \(\mathrm{Acc_{validate}}\) of 0.9238.
## 3 Results of Computational Experiments
### Results and Discussion  
The results of the experiments presented in Table 4 allow for the following conclusions and observations:  
* The application of the proposed mathematical model allows for a significant reduction of the space required to locate PLUs in the transportation and/or warehousing process, thus creating fewer required pallet positions. In the analyzed instances, 3-10 SPLUs were created from 8-20 PLUs, which means a two- to three-times reduction in the demand for pallet positions in logistics processes. A smaller number of PLUs translates directly into lower transportation costs in logistics networks, as well as less demand for rack slots in the process of storing PLUs.
* The smaller the number of _top_ status PLUs in the structure of stacked units, the better the chance of building a smaller number of SPLUs while maintaining all the constraints. In instances 1, 2, and 9 with at most two PLUs _top_ type, the number of SPLUs is three times less than the number of PLUs; in the other instances, the higher number of _top_ type PLUs results in at most 2-25 times reduction of the number of SPLUs compared to PLUs.
* The higher the values of weight and height of individual PLUs subjected to stacking, the fewer multi-layered SPLUs can be built. The structure of the SPLUs built depends on the specificity of the items located on the individual PLUs. In all instances (1-10), a higher degree of achievement of the max height parameter (82.0-100%) is obtained compared to the max weight parameter (60.6-86.6%). This means that the analyzed instances constitute a set of relatively light but hight products.
* The computing time to solve the problem of optimal stacking pallet load units is similar for all analyzed instances. It is 0.7-2.6 s. for 8-20 PLUs per instance, except 51.2 s. for 20 PLUs, see instance 5.
## 4 Reinforcement Learning for IoT Networking Management
### Related Research Studies
This approach optimizes transmission policy parameters such as the spreading factor, bandwidth, code rate, and carrier frequency to achieve a high QoS in LoRa communication. [PERSON] et al. [81] also proposed a DRL approach to enhance the routing scheme for IoT-enabled networks. The proposed DRL-based intelligent routing scheme reduces delay and improves the overall network lifetime. The authors suggested a novel clustering method that aims to prevent energy imbalances within the network. This scheme utilizes unequal clustering, multiobjective optimization, and load balancing to enhance the network performance and lifetime. In [82], the authors introduced a multi-hop state-aware routing approach based on traffic flow predictions. This strategy employs recurrent neural networks in conjunction with a deep deterministic policy gradient technique. [PERSON] et al. in [83] proposed a model that aims to avoid energy hole issues and inefficient data collection to preserve network stability while improving the routing performance using a Q-learning approach. The authors of [84] present a new adaptive routing protocol called AQ-Routing, which is based on RL to handle mobile ad hoc network (MANET) IoT systems. The AQ-Routing technique can detect each node's mobility level in the network. The mobility detection model allows each node to adjust its routing behavior based on the updated mobility factor. [PERSON] et al. in [85] addressed the issue of establishing extensive connectivity among IoT devices spanning a wide geographic region. The paper proposes an RL-based technique to address multi-hop data transmission challenges such as higher latency, increased interference, and reduced throughput. The proposed approach periodically updates the network's Q-matrix and makes relay device selections at discrete time intervals to optimize the cumulative reward value for chosen device gateway pairs. The authors of [86] proposed MeFi to address the challenges of maximizing the energy efficiency and network lifetime of battery-powered sensor networks. MeFi is based on the mean-field RL mechanism and considers the average behavior of the network nodes instead of dealing with each node individually. The authors leverage mean field theory to manage the vast state space caused by numerous devices by focusing on interactions among neighbors. The authors also introduce a prioritized sampling loop-free algorithm to prevent routing loops and discard suboptimal routing strategies. In [87], the authors proposed a routing scheme for Internet of Medical Things networks. The proposed scheme categorizes network traffic into three classes, customizing the QoS for each, and divides the network into zones to reduce message exchanges. Table 2 presents a summary of related work in the field, highlighting their contributions, application domains, and the algorithmic models they employed.
# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 4 Experimental Results  
In this section, we validate the performance of the proposed algorithm by conducting extensive experiments on 10 standard benchmark images shown in Figure 3. In our experiment, we first added synthetic white Gaussian noise with different variances into the test images. Then the proposed algorithm and four currently state-of-the-art denoising algorithms, including NLM, K-SVD, BM3D, expected patch log likelihood (EPLL) [21] algorithms, were used to denoise the test images. Finally, we compared the proposed algorithm with the fr state-of-the-art algorithms in terms of peak signal to noise ratio (PSNR), structural similarity (SSIM) [22], figure of merit (FOM) and visual quality.
## 4 Performance Analysis
### Simulations Results Corresponding to BS Motion  
#### 4.3.1 Analysis of \(D_{ba}\)  
As illustrated in Figure 4, regardless of the direction and magnitude of BS motion, the minimum value of \(D_{BA1}\) is approximately 50 m. From the point where \(D_{BA1}\) reaches its minimum, it decreases before that point and increases afterwards. \(D_{BA2}\) exhibits a similar pattern to \(D_{BA1}\). The common results of \(D_{BA}\) based on the direction of BS motion can be also summarized as follows:  
* The distance between an AUV and BS1 varies depending on BS1's motion relative to the AUV. When BS1 moves in the same direction as the AUV, the distance between them, measured before reaching the closest point (minimum distance), shows that \(D_{BA1F}\) is less than \(D_{BA1M}\). After surpassing this closest point, if BS1 continues in the same direction, the relationship flips, making \(D_{BA1F}\) greater than \(D_{BA1M}\), as depicted in Figure 4a. Conversely, if BS1 moves in the opposite direction to the movement of the AUV, the relationship between \(D_{BA1F}\) and \(D_{BA1M}\) around the closest point reverses compared to when BS1 moves in the same direction as the AUV, as shown in Figure 4b. This behavior demonstrates that the distance between the AUV and BS1 changes notably depending on the direction of BS1's motion. When they move towards one another, the distance decreases, and when they move away from one another, the distance increases.
* Similarly, for BS2, the relationship between \(D_{BA2F}\) and \(D_{BA2M}\) also varies at the point of minimum distance, depending on the direction of BS2's motion relative to the direction of the AUV's movement, as shown in Figure 4c,d.  
As the magnitude of BS motion increases, it can be observed that both \(D_{BA1M}\) and \(D_{BA2M}\) exhibit greater fluctuations compared to \(D_{BA1F}\) and \(D_{BA2F}\), as illustrated in all panels in Figure 4. This suggests that the distance between the AUV and the BS becomes more unpredictable and varies more widely when the BS is in motion. Moreover, as the conditions at sea become rougher (indicated by a higher sea state code), the fluctuations in these distances grow even more severe. Hence, the rougher the sea, the more unpredictable the distance between the AUV and the BS becomes, making it harder to maintain consistent communication.  
\begin{table}
\begin{tabular}{c l} \hline
**Parameters** & **Conditions** \\ \hline \(d_{A}\) & 50 m \\ \(v_{A}\) & 5 knots \\ \(r\) & 100, 300, 500, 1000 m \\ \(R\) & \(\sqrt{r^{2}+{d_{A}}^{2}}\) \\ \(\delta\) & 0.1 \\ \(L\) & \(L=(1-\delta)\times 2r\) \\ Sea State & 3, 6, 8 \\ \hline \end{tabular}
\end{table}
Table 3: Simulation conditions.
## 4 Discussion  
The results of the reservoir numerical simulation and the case study of the real gas field show that the proposed calculation models of drainage radius are very simple, efficient and highly accurate. However, it should be pointed out that these novel calculation models are derived by using the assumption of pseudo-steady flow. Also, the derived calculation models in this paper are suitable for a closed constant-volume gas reservoir with pseudo-steady flow. Like previous research [13], this study assumes that the boundary is circular. When the boundary is another shape, further research is needed. In addition, from Equations (5) and (14), it can be found that the calculation models all involve the physical properties of natural gas (i.e., \(\mu_{\text{g}}\) and \(C_{\text{g}}\)). Through the sensitivity analysis in Section 3.3, it can be seen that the impact of natural gas physical properties on the calculation results is greater than that of the formation coefficient (_Kh_), skin factor (S) and change rate in the gas rate (\(\beta\)). Therefore, PVT testing of natural gas is very important and has a significant impact on the calculation results. Moreover, for gas reservoirs with large changes in natural gas components, certain uncertainties will be brought to the calculation models.  
By observing the calculation procedures, it is clear that the calculation models of drainage radius of commingled gas production wells require at least two gas production profile tests. However, the accuracy of the tests is limited by the precision of test instruments. At the same time, sand production and liquid accumulation in the wellbore increase the difficulty in interpreting the test data, leading to certain errors in calculation results. Fortunately, according to the sensitivity analysis in Section 3.3, it is proved that the change rate of the gas rate (\(\beta\)) has relatively little impact on the calculation results drainage radius.