esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 2 Materials and Methods
### Environment Modeling
#### 2.1.1 Static Environment Modeling
Generally, the approximate curve substitute, such as the conic curve and the cubic curve in this paper, can be found and used in the environmental modeling for marine practice. Figure 1: (**a**) The potential contours of the total potential field for two point obstacles with different values of parameter \(\beta\). (**b**) The corresponding potential surface of the total potential field. Figure 2: (**a**) The potential features for a curve with the coding direction. (**b**) The surface of a potential field for a curve-shaped obstacle. Second, a concave polygon can also be divided into some convex polygons. Therefore, we can construct the potential field for polygons with concave polygon shapes by adding together the potential fields generated from each of the convex components [18]. For each face-shaped obstacle, the parameter \(\gamma\) can also be used to adjust the affected area and steepness of the potential field near the edge of the obstacle. A large \(\gamma\) can highly localize the effects of obstacles with low risks and increase the possibility to pass through a narrow passage. A small \(\gamma\) causes large clearance with dangerous obstacles and a large detour or even no passage between two closely located obstacles. Figure 4 shows a face obstacle's potential field with different values of parameter \(\gamma\). The total potential field of the whole static environment can be obtained by adding the potential fields of all the point obstacles, curve obstacles, and face obstacles. Figure 4: Potential field for a complex face obstacle with different values of parameter \(\gamma\). (**a**) and (**b**) are potential contours and potential surface for \(\gamma=5\) respectively. (**c**) and (**d**) are potential contours and potential surface for \(\gamma=1\) respectively. Figure 3: Construct diagram for a potential field of face or surface elements. Each type of spatial object may have special features for navigation. It is implied that these objects have a different range of effects to influence path planning for autonomous ships. If the critical value of the obstacle potential range is set to 0.01, the recommended parameters are set in Table 3 based on the safe distance to be maintained with the obstacle. Each side of a surface obstacle can also be set with a different \(\gamma\) based on the feature of the obstacle. For simplicity, this effect is handled with the same parameter \(\gamma\) for a surface obstacle in this table.
## 2 Proposed LED Driver and Analysis of Steady-State Operation
### Analysis of Operation Mode
#### 2.2.1 Operation Mode 1 (\(t_{0}<t<t_{1}\))  
At the moment before \(S_{2}\) is turned off, \(i_{p2}\) reaches a peak value, denoted as \(i_{p2,peak}\). When \(S_{2}\) is turned off, \(i_{p2}\) flows through \(D_{2}\) to charge \(C_{S2}\), and \(i_{U2}\) also flows into \(C_{B2}\) to charge \(C_{S2}\). In practice, the parasitic capacitance is very small. \(C_{S2}\) will be quickly charged to \(V_{B}+V_{o}\). After that, \(i_{p2}\) will flow through \(D_{2}\), \(C_{B2}\), \(D_{4}\), and \(C_{o}\), while \(i_{U2}\) will flow through \(D_{2}\).  
Figure 4: Schematic waveforms.  
\(D_{4}\) and \(C_{o}\), as shown in Figure 3a. The voltages and current equations for \(L_{p1}\) and \(L_{p2}\) are as follows.  
\[v_{p1}(t)=v_{rec}, \tag{4}\]  
\[i_{p1}(t)=i_{p1}(t_{0})+\frac{v_{rec}}{L_{p}}(t-t_{0}), \tag{5}\]  
\[v_{p2}(t)=-V_{B}-V_{o}+v_{rec}, \tag{6}\]  
\[i_{p2}(t)=i_{p2,peak}-\frac{(V_{B}+V_{o}-v_{rec})}{L_{p}}(t-t_{0}), \tag{7}\]  
The current \(i_{p1}\) keeps increasing linearly while \(i_{p2}\) starts to decrease linearly.  
From Figure 3a, the voltage equations of the primary and secondary windings of \(T_{1}\) are expressed as (8) and (9), respectively.  
\[v_{L1I1}+v_{T}=V_{B}, \tag{8}\]  
\[v_{L1Z}+v_{T}+V_{o}=0, \tag{9}\]  
Adding (8) and (9) gives  
\[v_{L1I}+v_{L1Z}+2v_{T}=V_{B}-V_{o}, \tag{10}\]  
Substituting (2) and (3) into (10) yields  
\[v_{T}=\frac{L_{m}}{L_{l}+2L_{m}}(V_{B}-V_{o}), \tag{11}\]  
Substituting (11) into (8) and (9), respectively, the leakage inductance voltages can be expressed as follows.  
\[v_{L1I1}=\frac{(L_{m}+L_{l})V_{B}+L_{m}V_{o}}{L_{l}+2L_{m}}, \tag{12}\]  
\[v_{L1Z}=\frac{-L_{m}V_{B}-(L_{m}+L_{l})V_{o}}{L_{l}+2L_{m}}, \tag{13}\]  
Equation (12) shows that \(v_{L1I1}\) is positive, so \(i_{L1I1}\) starts to rise from a negative value close to zero. After a period of time, \(i_{L1I1}\) rises to become positive. Equation (13) shows that \(v_{L1Z}\) is negative, and \(i_{L1Z}\) starts to decrease from the peak value. In other words, when \(S_{2}\) is turned off, \(i_{L1Z}\) decreases and \(i_{L1I1}\) increases, that is, the current originally flowing through the secondary winding of \(T_{1}\) gradually converts to flow through the primary winding.  
The boost converters would be designed to operate in DCM. When \(i_{p2}\) declines to zero, \(D_{2}\) turns off and the circuit enters the next operation mode.
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods  
### Azimuth Thruster  
Figure 1 shows an illustration of the azimuth thruster and its main parts. The thruster rotates itself 360 degrees around its vertical axis, which provides flexibility and thrust in every direction for the system. The thruster model type in this study is UUC 455 manufactured by Kongsberg Maritime Finland Oy. These heavy-duty L-drive azimuth thrusters are specifically designed for the dynamic positioning operation on offshore rigs and drill ships. They have Maximum Continuous Rating (MCR) 5.2-5.5 MW, input speed up to 720-750 rpm and propeller diameter 4.1 m. An ensemble of several thrusters (six typically) is usually mounted underneath a vessel, such as a drill ship.
# A Target Detection Method of Distributed Passive Radar without Direct-Path Signal
## 3 GLRT Detection Algorithm
### The Estimation of Time Delay of Arrival (TDOA)  
Because \(\widetilde{s}_{j}\) is the signal after removing the delay and Doppler shift and the estimation of TDOA can serve as the basis for positioning So, a new method of TDOA estimation is proposed in this paper. The steps are as follows.  
1. Generate the scheme of TDOA estimation;
2. Set the time delay interval \(b\), according to the distance between stations, set the search range as \([\tau_{\min},\tau_{\max}]\), and set the scheme \(S\). There are \(N_{r}-1\) auxiliary receivers, easy to know \(S\) is a finite collection. The number of elements is \((\tau_{\max}-\tau_{\min}+1)^{N_{r}-1}\), and \(S\) can be expressed as \[S = \big{\{}x\in R^{N_{r}-1}\big{|}x_{i}\in N,\tau_{\min}\leq x_{i} \leq\tau_{\max},i=1,2,\cdots,N_{r}-1\big{\}};\] \[= \big{\{}S_{1},S_{2},S_{3},\cdots,S_{(\tau_{\max}-\tau_{\min}+1)^{ N_{r}-1}}\big{\}}\]
3. For any \(x^{*}=(x_{1},x_{2},\cdots,x_{N_{r}-1})\in S\), represents the \(j\)th echo data from the receivers moving to the right \(x_{j-1}\cdot b\) time units;
4. Initialize the \(1\times(\tau_{\max}-\tau_{\min}+1)^{N_{r}-1}\) dimensional vector \(G=[g_{1},g_{2},g_{3},\cdots,\)\(S_{(\tau_{\max}-\tau_{\min}+1)^{N_{r}-1}}]\) and set all the entries in \(G\) to \(0\);
5. Estimate the TDOA according to the scheme;
6. Go through all the schemes in the set \(S\), and repeat the following (2) and (3) for each scheme until the collection \(S\) is traversed and step 3 is entered;
7. The echo data of each channel was arranged as \(1\times L\) dimensional vector in chronological order; that is \(\left(s_{j}^{k}\right)^{T}\in\mathbb{C}^{1\times L}\), the echo data of \(N_{r}\) channel was merged into a \(N_{r}*L\) dimensional matrix \(\mathbf{X}\);
8. Make a calculation of \[\xi=-\frac{1}{\sigma^{2}}\lambda_{1}(\mathbf{X}\mathbf{X}^{H})\]
9. Put \(\xi\) in the corresponding position of vector \(G\), let us say \(g_{i}=a,1\leq i\leq(\tau_{\max}-\tau_{\min}+1)^{N_{r}-1}\);
10. Traverse the vector \(G\) to find the maximum value, as shown in (17); \[z=\max_{i\in\{1,2,\cdots,(\tau_{\max}-\tau_{\min}+1)^{N_{r}-1}\}}G_{i}\] (17)
11. Record the position of the maximum value in \(G\), and find out the corresponding scheme of the same position as in \(G\) and \(S\), so as to determine the delay time \(\tau_{0}\), as shown in Equation (18). \[\tau_{0}=\underset{i\in\{1,2,\cdots,(2M+1)^{K-1}\}}{\text{argmax}}G_{i}\] (18)
## 3 Methods
### Extraction of Human Pose Euler Angles
#### 3.2.2 Euler Angle Correction for Quaternion and Rodriguez Parameters  
Given the roll \(\varnothing\), pitch \(\theta\), and yaw \(\varphi\) Euler angles for each body part, these can be converted into rotation quaternions. The quaternion representing the orientation of a body part in the common coordinate system would be in the form:  
\[q=cos\bigg{(}\frac{\varnothing}{2}\bigg{)}cos\bigg{(}\frac{\theta}{2}\bigg{)} cos\bigg{(}\frac{\varphi}{2}\bigg{)}+sin\bigg{(}\frac{\varnothing}{2} \bigg{)}sin\bigg{(}\frac{\theta}{2}\bigg{)}sin\bigg{(}\frac{\varphi}{2}\bigg{)} sin\bigg{(}\frac{\theta}{2}\bigg{)}sin\bigg{(}\frac{\varphi}{2}\bigg{)} sin\bigg{(}\frac{\varphi}{2}\bigg{)}k \tag{9}\]  
\[\left\{\begin{aligned} &\varnothing=atan2\big{(}2(q_{0}q_{3}+q_{1}q_{2}),1- \big{(}q_{2}{}^{2}+q_{3}{}^{2}\big{)}\big{)}\\ &\theta=asin(2(q_{0}q_{2}+q_{1}q_{3}))\\ &\varphi=atan2\big{(}2(q_{0}q_{1}+q_{2}q_{3}),1-\big{(}q_{1}{}^{2}+ q_{2}{}^{2}\big{)}\big{)}\end{aligned}\right. \tag{10}\]  
where, \(q_{0}\), \(q_{1},q_{2}\), _and_\(q_{3}\) are the components of the quaternion.  
Compared to the Euler angle method, the quaternion description method further simplifies the calculation process and effectively avoids the singularity problem of the Euler angle due to a trigonometric function operation, but quaternions possess redundant parameters and quaternion normalization constraints. Quaternion tracing was compared to the Euler angle method singularity problem of the angle function operation. Lastly, to facilitate the extraction of human activity posture features, a dimensionality reduction technique is applied to the optimized Euler angles obtained through the above steps. The resulting reduced Euler angles are defined as the attitude angle, denoted as attitude. The formula for computing the attitude angle is as follows:  
\[attitude=arccos(cos\mathcal{O}cos\theta cos\varphi+sin\mathcal{O}sin\theta sin \varphi) \tag{11}\]  
where, \(\mathcal{O}\), \(\theta\), and \(\varphi\) are the roll, pitch, and yaw angles, respectively. This formulation combines the three-dimensional Euler angles into a single one-dimensional attitude angle, providing a concise representation of the overall posture.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 4 Future Directions
### An Accurate Rotor Estimation Solution in Whole-Speed Range  
Position sensorless technology for the full speed range is an ongoing goal. Many methods demonstrate accurate position estimation in part of the velocity range, which is also of great significance for the development of position sensorless methods. However, this limits the practical application of the algorithm in that the motor is required to be in the full speed range. It is necessary to seek rotor position estimation methods in a wider speed range.
## 1 Introduction
In contrast, SC-FDE features a lower PAPR, making it a more appropriate choice for uplink communications. This paper evaluates the performance of four different receivers--ZF, MMSE, MRC, and EGC [20; 21]--providing a comparative analysis of their effectiveness. The contributions of this work are as follows:  
* We propose a neural-network-based interference cancellation approach for LISs, eliminating the need for the iterative decoding traditionally used with MRC/EGC receivers. * We demonstrate that MRC and EGC receivers enhanced with neural networks outperform conventional ZF/MMSE receivers or MRC/EGC with iterative interference suppression, achieving a better balance between computational efficiency and performance. * We show that our method avoids the need for channel matrix inversion, reducing computational complexity while maintaining a competitive performance. The structure of this paper is organized as follows: Section 2 describes the system and signal models; Section 3 introduces the neural-network-based interference cancellation approach; Section 4 presents and analyzes the performance results; and Section 5 concludes the paper.
## 4 Roles of PPK2s in Bacterial Physiology and Virulence
### Biofilms  
The Kornberg lab's initial discovery that _P. aeruginosa_ PPK2A preferentially synthesizes GTP--a precursor for making the alginate found in Pseudomonas biofilms--led to speculation that PPK2A was important for biofilm formation [34]. Our group provided the first experimental evidence in support of this hypothesis, demonstrating that a \(\Delta\)_ppk2\(\Delta\)_ppk2B\(\Delta\)_ppk2C_ mutant of _P. aeruginosa_ PA14 was attenuated in biofilm formation relative to the WT, roughly mirroring the biofilm defect observed in the \(\Delta\)_ppk1_ mutant. Biofilm formation of the \(\Delta\)_polyP_ strain lacking all four _ppk_ genes was even further attenuated compared to \(\Delta\)_ppk2\(\Delta\)_ppk2B\(\Delta\)_ppk2C_ or \(\Delta\)_ppk1_[57]. Further research is needed to decipher the individual contribution of PPK2A, PPK2B, and PPK2C in biofilm formation. Similarly, a _ppk2_ transposon mutant of _M. tuberculosis_ strain CDC1551 was shown to be defective in pellicle biofilm formation [74]. However, in a different study, _ppk2_ deletion in _M. tuberculosis_ strain H37 Rv did not appear to affect biofilm formation upon visual inspection, although the authors did not quantify the biofilm [55]. This disagreement could be explained by the use of different parental strains, or the differing means of _ppk2_ gene inactivation. Surprisingly, a _ppk2_ mutant of _C. jejuni_ formed significantly more biofilm than the WT [48]. Caution must therefore be taken when generalizing the relationship between PPK2 and biofilm from one species or strain to another.
## 3 Experiments and Results
### The Other Experiments  
#### 3.5.1 TNO Dataset  
In the infrared and visible image fusion dataset, there are 21 pairs of different infrared and visible image images in TNO dataset. In this paper, these images are converted into grayscale images for experiments and comparison. The specific experimental results are as follows:  
The red font in Table 4 indicates that the network model proposed in this paper has the highest score in 4 of the 7 evaluation indicators. In Figure 6, the clouds in the sky cannot be seen in the red box of the visible light image, but the trees in the white box can be clearly seen. In the red box of the infrared image, you can see many clouds and contour information, but you can't see trees. Other images are obtained by various image fusion algorithms. There are many noises in CBF. IFCNN and DenseFuse retain more details of trees and clouds. The images fused by the network model proposed in this paper retain both the cloud layer information in the red box of the infrared image and the tree information in the white box of  
Therefore, the network model proposed in this paper can better retain more information in infrared images and infrared images.  
Besides, to prove the effectiveness of the proposed network model, we also fuse and test other images, such as images with many exposures and images with different focuses. The specific experimental results are as follows:
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### IoMT Architecture  
As the demand for health monitoring increases, this work focuses on the design of network architecture and data analysis techniques for the IoMT system, consisting of sensor nodes, relay nodes, and the base station. Through priority-based clustering, weight assignment, data compression, and scheduling algorithms, the performance of group monitoring is examined in terms of transmission load and latency.  
Most of the current IoMT systems are divided into four layers, as shown in Figure 2, starting from personal physiological signal collection to analysis and ending in data storage and management. Observe that the first architecture component is the device layer, which uses wearable sensors to collect physiological signals from patients. The patients are grouped into clusters based on disease severity. Then, the wearable device transmits the data to the second architecture component, the broker layer, over wireless communication protocols such as Bluetooth or Wi-Fi. Due to the processing and storage limitations of the device, the members in the cluster first transmit the compressed physiological signals to local controllers (i.e., a CH or an inter-CH), and then the more powerful relay node performs decompression and data analysis. The details of compression and clustering algorithms are discussed in the following subsections, respectively. The third architecture component is the network layer, where the relay node sends the data to the database through the network after decompression and data analysis. The last architecture component is the management layer. Through the collected and analyzed data, health care professionals can monitor the physical conditions of all patients and handle the emergencies.
## 2 General Design and Features of the Instrument v2021
### Total Cost and Assembly Process
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Function** & **Price (USD)** & **Assembly Steps** \\ \hline Artemis Global Tracker & main board, MCU, GNSS, Iridium & 375 & ready to use \\ GNSS + Iridium antenna & passive antenna & 65 & screw on SMA cable \\ SMA extension cable 25 cm & extension cable for antenna & 5 & screw on tracker \\ Qwic power switch & power on and off 9-dof & 7 & disable LED, connect 9-dof and tracker \\ ISM330 DHCX + LIS3 MDL & 9-dof sensor & 18 & connect to power switch \\ Qwic cables (x2) & connect tracker, 9-dof, switch & 3 & connect power switch and 9-dof \\
3.3V Regulator S7V8F3 & 3.3 V buck converter & 10 & solder to battery and tracker \\
2 \(\times\) D cell holders & house and connect batteries & 15 & solder to 3.3 V regulator \\
2 \(\times\) SAFT LSH20 & power supply & 35 & put in cell holders \\ need MDDR-DT-20-35-F & magnetic switch & 3 & solder between battery and regulator \\ magnet & turn magnetic switch on/off & 1 & mount outside housing \\ housing box & housing, IP68 & 20 & mount the electronics inside \\ misc: glue, wire & small extras & 5 & get the design assembled \\ total & fully functional instrument & 562 & 0.5 h/instruments, producing 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A representative list of components needed to build an instrument monitoring drift (GNSS) and wave activity (9-dof sensor). The assembly time for a single instrument, when assembling a series of 10 instruments in bulk, is about 0.5 h once the user is familiar with the design. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Functionality** & **Credits/Message** & **Messages/Hour (Default)** & **Price/Month (USD)** \\ \hline iridium subscription fee 1 month & N/A & N/A & 16 \\ GNSS position data & 2 & 0.3 & 26 \\ wave spectrum data & 3 & 0.5 & 66 \\ total & N/A & N/A & 108 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the Iridium communication costs. The prices correspond to the ones offered by RockSeven at the time of writing this manuscript. It is possible to modify the rate of data collection by either using different setups when one programs the instrument firmware, or by sending a measurement rate update command to the instrument through iridium. when little activity of interest is happening will allow to significantly reduce the total cost of ownership.
## 2 Mathematical Description of the Proposed Hybrid System Components
### Wind Energy System  
Based on the basics of aerodynamics, wind power can be presented as [24]:  
\[P_{wind}=\left\{\begin{array}{cl}0,&V(t)\leq V_{ci},\ V(t)\geq V_{co}\\ a\times V(t)^{3}-b\times P_{r},&V_{ci}<V(t)<V_{r}\\ P_{r},&V_{r}\leq V(t)<V_{co}\end{array}\right. \tag{3}\]  
Figure 3: Power management of the PV/WT/diesel/battery energy storage system (BESS), PV/diesel/BESS and WT/diesel/BESS hybrid RES.  
where \(V\) represents wind speed, \(P_{r}\) is the rated power of wind, \(V_{ci}\), \(V_{co}\) and \(V_{r}\) are the cut-in, cut-out, and rated wind speed, respectively. \(a\) and \(b\) are two constants, which can be expressed as:  
\[\left\{\begin{array}{l}a=P_{r}/\left({V_{r}}^{3}-{V_{ci}}^{3}\right)\\ b={V_{ci}}^{3}/\left({V_{r}}^{3}-{V_{ci}}^{3}\right)\end{array}\right. \tag{4}\]  
The rated power of wind is calculated as given in the following equation:  
\[P_{r}=\frac{1}{2}\times\rho\times{A_{wind}}\times{C_{p}}\times{V_{r}}^{3} \tag{5}\]  
where \(\rho\) represents the air density, \(A_{wind}\) is the swept area of the wind turbine, \(C_{p}\) is the maximum power coefficient ranging from 0.25% to 0.45%.
## 6 Conclusions and Future Work  
In this paper, we have proposed a sparsity-based method to recover the missing data in wireless sensor networks. Instead of investigating the correlation in local neighboring sensors, the proposed approach exploits the sparsity of network data by solving the \(l_{1}\) norm minimization problem. Both synthetic and real data simulation results demonstrate that the proposed approach can successfully recover the missing data and that there exists a flexible range of missing rates where the proposed method is robust to missing block size, as long as the network data have the sparsity property.  
Although the sparsity-based interpolation shows advantages over KNN, for some other applications and under different assumptions, it could be wed with KNN as well as other interpolation methods to make full use of their respective advantages. For example, one limitation of sparsity-based interpolation is that the number of available samples should be enough to estimate the DCT coefficients. Based on the observation that KNN can recover the missing samples reasonably when the data only contains low-frequency components and size of missing blocks is not too large as shown in Simulation part, KNN could be utilized to estimate the low-frequency components and sparsity-based interpolation is employed to estimate the high-frequency components. This potentially requires less available samples for the sparsity-based method since the unknowns for \(l_{1}\) minimization are reduced. For the future work, an extension of the proposed method for the irregular grid by dividing the whole network field into cells will be further investigated. Also we will extend it to 3-D case where the third dimension is time.