esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Interference and energy holes formation in underwater wireless sensor networks (UWSNs) threaten the reliable delivery of data packets from a source to a destination. Interference also causes inefficient utilization of the limited battery power of the sensor nodes in that more power is consumed in the retransmission of the lost packets. Energy holes are dead nodes close to the surface of water, and their early death interrupts data delivery even when the network has live nodes. This paper proposes a localization-free interference and energy holes minimization (LF-IEHM) routing protocol for UWSNs. The proposed algorithm overcomes interference during data packet forwarding by defining a unique packet holding time for every sensor node. The energy holes formation is mitigated by a variable transmission range of the sensor nodes. As compared to the conventional routing protocols, the proposed protocol does not require the localization information of the sensor nodes, which is cumbersome and difficult to obtain, as nodes change their positions with water currents. Simulation results show superior performance of the proposed scheme in terms of packets received at the final destination and end-to-end delay.  
**Keywords:** underwater; routing; protocol; interference; energy holes; adaptive transmission range +
Footnote †: journal: Sensors
## 4 Analysis
### Minimum Orbit Altitude  
In order to facilitate common-viewing between stations, it is imperative to predefine a minimum orbital altitude for the satellite, considering the distances between these stations. The assumption here is that the satellite can be effectively recorded by ground laser stations within a specified maximum observable elevation angle. Assuming that the average radius of the Earth is 6371 km, the minimum distance between two ground stations is set at 1500 km, while the maximum distance is approximately half the circumference of a circle with a radius of 6371 km. Therefore, when the satellite is positioned at the midpoint of the line connecting two stations, the altitude it holds corresponds to the minimum satellite orbit altitude necessary for the initiation of common-view between the two stations.  
Assuming the maximum observable elevation angles of the satellite by ground laser stations are \(10^{\circ}\), \(15^{\circ}\), and \(20^{\circ}\), respectively, the curve depicting the variation of the satellite's lowest orbit altitude concerning station distance was computed. As the determination of the satellite's observable elevation angle is established and the distance between ground laser stations increases, the achievable minimum orbit altitude for the common-view of the satellite also exponentially increases. At a \(10^{\circ}\) observation angle and an inter-station distance of 1500 km, the minimum satellite orbit altitude measures 181.52 km. When the distance falls below 4814 km, the satellite's minimum orbit altitude is within the low Earth orbit range. Between 4814 km and 12,100 km, the satellite's minimum orbit altitude shifts to the medium Earth orbit range. Within the range of 3540 km to 11,300 km, the minimum satellite orbit altitude falls within the medium Earth orbit range. These changing trends are shown in Figure 4. When the satellite orbit's altitude increased up to 20,000 km, orbital altitudes which are common for navigation satellites, it will meet the requirements for common-view observation over continental scales directly. Therefore, considering the launch costs and the operational lifespan in orbit, meticulous satellite orbit design is essential for enhancing both the accuracy and coverage of common-view time transfer.  
Figure 4: Minimum satellite orbit altitude changes with distance between stations.
# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 3 Methods for Prediction of BBN
### Numerical Methods
Figure 17: SPL spectra for (**a**) hovering and (**b**) \(U_{\infty}=15\) m/s (black line is experimental data; blue line is numerical simulation of mesh1; red line is numerical simulation of mesh2) [66]. Figure 18: Broadband noise PSD at observer points (**a**) \(\theta=0^{\circ}\) and (**b**) \(\theta=-45^{\circ}\)[67]. Recently, more high-fidelity research for wake/turbulence ingestion noise has been conducted. [PERSON] et al. [71] utilized the LBM coupled with the Ffowcs-Williams and Hawkings acoustic analogy to predict cylinder-induced turbulence ingestion noise of propeller. Their numerical method revealed that the interaction between the cylinder wake and rotor led to an increase in the broadband noise. Although the SPL spectrum indicated an underprediction of broadband noise, particularly at low frequencies, compared with the experiment, the method still demonstrated a strong capability in predicting TIN.
## 3 Optimization Framework
### POD-Based Dimension Reduction Technique and an Adaptive Sampling Method
#### 3.4.2 The Adaptive Sampling Method  
To reduce the total number of sample points, an adaptive sampling approach was used. Firstly, fewer sample points were used for initial sampling, which is insufficient for the accuracy requirement. New sample points were then added after POD analysis. The approach places more points around regions of initial optimum designs by learning information from previous data through POD analysis. The initial sample points usually contain better points for the multimodal optimization problem. However, which point among these is near the optimal solution is uncertain. Our solution is to add new points around several better points of the initial sample, as shown in Figure 8. Consequently, the method has great potential to decrease the total sample points required and further reduce computational costs.
## 2 Induction Heating and Numerical Simulations
### Comparison of Electromagnetic Tasks in 2D and 3D  
Before continuing with the thermal analyses of induction heating of the investigated WNiCo workpieces, the optimization of the designed 3D model was performed via mutual comparison of the results of 2D and 3D electromagnetic parameter calculations. The comparison of the predictions performed using both the pieces of numerical software showed sufficient correlation; despite the fact that the Forge NxT software computations were performed in 3D and the ANSYS-Maxwell computations were done in 2D, the results exhibited only minor deviations. Both the simulations of magnetic field intensity exhibited a homogeneous distribution of this parameter along the workpiece (see Figure 4a,b for magnetic field intensity distributions according to the 2D and 3D models (cutting plane), respectively). However, its distribution showed certain inhomogeneity and a greater occurrence of the maximum values in the 3D model (Figure 4b). The distribution of magnetic induction was comparable to the distribution of magnetic field intensity in both the models (not shown here). Both the magnetic parameters also non-negligibly affected the distribution of current density. The only visible difference revealed by the comparison of both the models was in the skin effect, which appeared to be approx. 30% greater in the 2D model. However, this difference can primarily be attributed to different mesh densities. The last investigated parameter was the Joule heat, i.e., heat power, the distribution of which exhibited a character similar to the distribution of current density in both the models (see Figure 4c,d for heat power distributions according to the 2D and 3D (cutting plane) models, respectively). In the 3D model (Figure 4d), however, the heat penetration depth was greater and its distribution along the workpiece was less homogeneous than in the 2D model (Figure 4c).  
Figure 3: Initial set-up of induction heating: 3D model (Forge NxT) (**a**); detail of mesh of 3D model (**b**); 2D model (Ansys-Maxwell) (**c**); detail of mesh of 2D model (**d**). Closer details of the 3D models of heated workpiece (**e**) and the inductor coil (**f**).
## 2 Kinematic Model  
As shown in Figure 1, the local coordinate frame of the service robot chassis is \(X_{\text{R}}PY_{\text{R}}\), the global coordinate frame is \(X_{\text{I}}OY_{\text{I}}\), and the point \(P\) is the reference point on the chassis.  
In the global coordinate frame, the position \(P\) is determined by \(X_{\text{I}}\) and \(Y_{\text{I}}\)[17; 18; 19; 20], and the position of the chassis in the local coordinate frame is:  
\[\xi_{\text{R}}=R(\theta)\bullet\xi_{\text{I}} \tag{1}\]where \(\theta\) is the angle between the global coordinate frame and the local coordinate frame, \(\xi_{\rm I}=\left[\begin{array}{cc}x_{\rm I}&y_{\rm I}&\theta_{\rm I}\\ \end{array}\right]^{\rm T}\) is the position of the robot in the global coordinate frame, and \(\xi_{\rm R}\) is \(\xi_{\rm R}=\left[\begin{array}{cc}x_{\rm R}&y_{\rm R}&\theta_{\rm R}\\ \end{array}\right]^{\rm T}\). \(R(\theta)\) is the rotation matrix. \(R(\theta)\) is:  
\[R(\theta)=\left[\begin{array}{ccc}\cos\theta&\sin\theta&0\\ -\sin\theta&\cos\theta&0\\ 0&0&1\\ \end{array}\right] \tag{2}\]  
The rolling constraint of the Mecanum wheel forces all rollers to move along the ground direction. There must be a certain number of rollers rotating so that there is pure rolling at the contact point [10; 21]. The velocity direction of the wheels is decomposed and deduced:  
\[jR(\theta)\overset{\bullet}{\xi_{\rm I}}=r\overset{\bullet}{\varphi}\cos\gamma \tag{3}\]  
where \(j=\left[\begin{array}{cc}\sin(\alpha+\beta+\gamma)&-\cos(\alpha+\beta+ \gamma)&-l\cos(\beta+\gamma)\\ \end{array}\right]\), as shown in Figure 2, \(l\) is the distance between every Mecanum wheel and the point \(P\), and \(r\) is the radius of the Mecanum wheel.  
The rolling constraints of an individual Mecanum wheel can be grouped into the single Equations (4) and (5).  
\[J_{\rm I}R(\theta)\overset{\bullet}{\xi_{\rm I}}-J_{2}\overset{\bullet}{ \varphi}=0 \tag{4}\]  
\[\overset{\bullet}{\varphi}=J_{2}{}^{-1}J_{\rm I}R(\theta)\overset{\bullet}{ \xi_{\rm I}} \tag{5}\]  
Figure 1: Global coordinate and local coordinate frames.  
Figure 2: A schematic diagram of the motion of a single wheel.  
where \(\overleftarrow{\varphi}(t)\) is the velocity function of the horizontal axis of the Mecanum wheel.  
\[J_{1}=\left[\begin{array}{ccc}\sin(\alpha_{1}+\beta_{1}+\gamma_{1})&-\cos( \alpha_{1}+\beta_{1}+\gamma_{1})&-l\cos(\beta_{1}+\gamma_{1})\\ \sin(\alpha_{2}+\beta_{2}+\gamma_{2})&-\cos(\alpha_{2}+\beta_{2}+\gamma_{2})&-l \cos(\beta_{2}+\gamma_{2})\\ \sin(\alpha_{3}+\beta_{3}+\gamma_{3})&-\cos(\alpha_{3}+\beta_{3}+\gamma_{3})&-l \cos(\beta_{3}+\gamma_{3})\\ \sin(\alpha_{4}+\beta_{4}+\gamma_{4})&-\cos(\alpha_{4}+\beta_{4}+\gamma_{4})&- l \cos(\beta_{4}+\gamma_{4})\end{array}\right] \tag{6}\]  
\[J_{2}=\left[\begin{array}{ccc}r\cos\gamma_{1}&&&\\ &r\cos\gamma_{2}&&\\ &&r\cos\gamma_{3}&\\ &&&r\cos\gamma_{4}\end{array}\right] \tag{7}\]  
Assuming \(J={J_{2}}^{-1}J_{1}R(\theta)\), the velocity is:  
\[\overleftarrow{\xi}_{1}=(J^{\rm T}J)^{-1}J^{\rm\bullet}\overleftarrow{\varphi} \tag{8}\]
## 2 Study Area and Period  
Because of the different climates, vegetation covers and human activity intensity in various regions of the World, it is difficult to derive CO\({}_{2}\) concentrations using a single metric. To increase the derived accuracy of the CO\({}_{2}\) concentration, we divided the World into five regions according to the continents: Africa, Australia, Eurasia, North America and South America (Figure 1). For each continent, we used different variables to assess CO\({}_{2}\) concentrations. The annual NDVI in 2011 was 0.33, 0.36, 0.33, 0.41 and 0.59 for Africa, Australia, Eurasia, North America and South America, respectively. According to the ESA 2009 GlobCover data [34], which has a 300-m resolution, the bare area (the sum of sparse vegetation and bare area) rate in Africa, Australia, Eurasia, North America and South America is 36.77%, 41.79%, 31.20%, 10.9% and 15.3% respectively. The World's largest desert, the Sahara, is located in northern Africa, leading to a lower vegetation cover in northern Africa. Australia is the driest continent in the World [35] and from Figure 1 we can see that the vegetation cover in all of Australia is very low. In Eurasia, the bare area (including the Arabian Desert and the Gobi Desert) is mainly distributed in western and central Asia. The Amazon River Basin in South America is mainly covered by evergreen tropical rainforests, which have a higher vegetation cover [36]. The total forest area in South America is approximately 9.5 \(\times\) 10\({}^{6}\) km\({}^{2}\)[37]. Moreover, South America is located near the Equator, and the higher annual temperatures and precipitation contribute to the higher NDVI in this region.  
This study was conducted for data from June 2009 to November 2011 because the GOSAT TANSO data from April 2009 to November 2011 were available, but no such data exists for May 2009. The MODIS products for this time period were all available.
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
###### Abstract  
Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.  
multi-view learning; subspace representation; graph learning; one-step clustering +
Footnote †: journal: Applied Sciences
## 8 Practical Considerations for Real-World Applications
### Empirical Performance Comparisons of DRL Methods  
To complement the theoretical discussions, we summarize recent empirical evaluations of DRL-based mobile robot path-planning techniques. Table 1 presents key performance metrics across different DRL methods.  
[PERSON] et al. [81] evaluated Deep Q-Networks (DQN) and its variants (DDQN) for robot navigation in cluttered environments, reporting an average success rate of 78%. However, these value-based methods required longer training times (24 h on average) and exhibited moderate robustness to sensor noise. Conversely, [PERSON] et al. [53] found that policy-based techniques like Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) achieved success rates above 88% while converging in just 12 h.  
Further studies [PERSON] et al. [82], [PERSON] [83] showed that actor-critic methods (DDPG, SAC) outperformed value-based methods, achieving a 91% success rate with better generalization in dynamic environments. Notably, [PERSON] et al. [85] demonstrated that an improved  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**DRL Method**} & **Success** & **Training** & **Robustness** \\  & & **Rate (\%)** & **Time (h)** & **to Noise** \\ \hline
[81] & DQN, DDQN & 78 & 24 & Moderate \\ \hline
[53] & PPO, SAC & 88 & 12 & High \\ \hline
[82] & DDPG, SAC & 91 & 10 & High \\ \hline
[83] & DQN variants & 85 & 18 & Moderate \\ \hline
[84] & Improved DQN & 82 & 14 & High \\ \hline
[85] & SAC (Improved) & 95 & 9 & Very High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance Comparison of DRL Methods in Mobile Robot Path Planning.  
SAC algorithm yielded the highest success rate (95%) and exhibited superior robustness, making it a promising approach for real-world deployment.
## 4 Discussion
### Influence of Spectral Resolution on Transmittances  
The absorption depth is related to the spectral resolution of the data. In this study, the irradiance and radiance were originally simulated with FWHM of 1 cm\({}^{-1}\) (about 0.06 nm at 760 nm) by MODTRAN. To investigate the influence of spectral resolution on transmittance, the original spectra were convolved with a series of Gaussian functions with different FWHM (from 0.06 nm to 0.5 nm) to calculate the transmittances.  
Figure 11 shows the upward atmosphere transmittances at 760.6 nm from MODTRAN simulations with different FWHM under a fixed atmospheric condition (mid-latitude summer profile, AOD550 = 0.1, VZA = 25\({}^{\circ}\), sensor's height is 25 m, and surface altitude is 1.5 km). The transmittances were calculated two ways: \(<\)\(L_{sensor}\)/\(L_{canopy}\)\(>\) (\(T_{\uparrow}\), red points) and \(<\)\(L_{sensor}\)\(>\)/\(<\)\(L_{canopy}\)\(>\) (\(T_{\uparrow}^{\prime}\), blue points). The results show that the transmittance inside the O\({}_{2}\)-A absorption band increases with the decrease of spectral resolution, and the difference between \(<\)\(L_{sensor}\)/\(L_{canopy}\)\(>\) and \(<\)\(L_{sensor}\)\(>\)/\(<\)\(L_{canopy}\)\(>\) is obvious.  
This study aims to provide a simple and operational resolution for the process of tower-based measurements. The look-up tables were generated using data with the same spectral resolution of the spectrometers we used for field measurement. For application on different spectrometers, the specific dataset with corresponding spectral resolution was suggested to be simulated.  
Figure 10: Downwelling and upwelling radiance at O\({}_{2}\)-A band measured on the ground and on the tower (without atmospheric correction) at 13:01 on 7 September 2018, at the Daman site.
## 2 Materials and Methods
### The HJA-N Model
\begin{table}
\begin{tabular}{c c} \hline \hline
**Equation** & **Defines** \\ \hline \(\dfrac{dS_{N,R}}{dt}=D_{N_{N}}+D_{N_{N}}+D_{N}-M_{N_{N},R}-M_{N_{N},R}-T_{N}+I_{N_ {N},R}\) & Nitrogen in the root zone SOM \\ \(\dfrac{dS_{C,R}}{dt}=T_{C}-R_{C,R}\) & Carbon below the root zone SOM \\ \(\dfrac{dS_{N,R}}{dt}=T_{N}-M_{N_{N},R}-M_{N_{N},R}+I_{N_{N},R}\) & Nitrogen below the root zone SOM \\ \(\dfrac{dW_{N_{N},R}}{dt}=D_{N_{N},R}+M_{N_{N},R}-U_{N_{N},R}-D_{N_{N},R}-I_{N_ {N},R}-Q_{N_{N},R}\) & Dissolved available nitrogen in the root zone \\ \(\dfrac{dW_{N_{N},R}}{dt}=D_{N_{N}}+M_{N_{N},R}-D_{N_{N},R}-Q_{N_{N},R}\) & Dissolved unavailable nitrogen in the root zone \\ \(\dfrac{dW_{N_{N},R}}{dt}=Q_{N_{N},R}+D_{N_{N},R}+M_{N_{N},R}-Q_{N_{N},R}-D_{N_ {N},R}-I_{N_{N},R}\) & Dissolved available nitrogen below the root zone \\ \(\dfrac{dW_{N_{N},R}}{dt}=Q_{N_{N},R}+M_{N_{N},R}-D_{N_{N},R}-Q_{N_{N},R}\) & Dissolved unavailable nitrogen below the root zone \\ \(\dfrac{dW_{N_{N},R}}{dt}=Q_{N_{N},R}+M_{N_{N},R}-D_{N_{N},R}-Q_{N_{N},R}\) & Dissolved available nitrogen \\ \(\dfrac{dW_{N_{N},R}}{dt}=Q_{N_{N},R}+M_{N_{N},R}-D_{N_{N},R}-Q_{N_{N},R}\) & in the aquatic environment \\ \(\dfrac{dM_{N}}{dt}=Q_{N_{N},R}+M_{N_{N},R}-Q_{N_{N},R}\) & Dissolved unavailable nitrogen \\ \(\dfrac{dM_{N}}{dt}=BI_{N}+I_{N_{N},R}-M_{N_{N},R}-M_{N_{N},R}-P_{N}\) & Aquatic Biomass (particulate, algae,benthic) \\ \(\dfrac{dW_{N,C}}{dt}=DP_{W}-TF_{w}-E_{W}\) & Water in the canopy \\ \(\dfrac{dW_{W,R}}{dt}=TF_{W}-ET_{w}-VF_{W}\) & Water in the root zone \\ \(\dfrac{dW_{W,R}}{dt}=IN_{W,R}-LF_{w}-SF_{w}\) & Water below the root zone \\ \(\dfrac{dW_{W,R}}{dt}=LF_{w}+SF_{W}+HE_{W,S}-HE_{W,HY}\) & Water in the riparian/hyporheic zone \\ \(\dfrac{dW_{W,S}}{dt}=HE_{W,HY}-HE_{W,S}-Q_{W,S}\) & Water in the channel \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rate** & **Definition** & **Units** \\ \(DP_{W,C}\) & Deposition of water (precipitation) & m/day \\ \(TF_{w,C}=W_{w,C}k_{w,s}\) & Throughfall from canopy & m/day \\ \(E_{r,C}=W_{r,C}k_{r,s}\) & Evaporation from canopy & m/day \\ \(ET_{W,R}\) & Evapotranspiration from root zone & m/day \\ \(VF_{r,R}=W_{r,R}k_{w,s}\) & Vertical movement of water within the root zone & m/day \\ \(LF_{r,s}=W_{r,R}k_{w,s}\) & Long (time) flowpaths of water below the root zone & m/day \\ \(SF_{r,s}=W_{r,R}k_{r,s}\) & Short (time) flowpaths of water below the root zone & m/day \\ \(HE_{w,ES}=W_{r,ES}k_{w,ES}\) & Hyporheic exchange of water from the stream & m/day \\ \(HE_{w,HY}=W_{w,HY}k_{w,HY}\) & Hyporheic exchange of water from the Hyporheic zone & m/day \\ \(Q_{r,s}=W_{r,R}k_{r,s}\) & Discharge of water from the stream & m/day \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rate terms comprising the hydrological model. See Table 7 for parameter definitions.
## 3 Results  
### Search and Selection  
The initial literature search identified 946 potentially eligible publications up to 31 st December 2020 (91 articles in PubMed, 728 articles in Scopus, and 127 articles in Web of Science). One hundred and eighty-three duplicated records were removed. After the screening of the titles and abstracts, the researchers excluded articles that did not study the effect of treatment combining a 9300 nm CO\({}_{2}\) laser and fluoride on the prevention of caries, along with papers that were literature reviews, case reports, conference papers, and book sections. Full-text papers were obtained for the remaining 58 publications. A search of the references of the selected studies did not identify any additional publications that met the inclusion criteria. Fifty-three studies were excluded according to the exclusion criteria. The remaining five studies were found to meet the eligibility criteria and were included in this systematic review. All of them were in vitro studies. Figure 1 is a flowchart of the selection process for studies according to the PRISMA statement on literature searches [32].