esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Assumptions  
### Network Model  
This paper presents a large-scale 3D network model for UWSNs where the underwater sensor nodes are randomly deployed in an underwater monitoring area. Figure 1 illustrates the network model and the description is as follows:  
1. The 3D underwater network is evenly divided into small cubes and each cube is regarded as a cluster.
2. Two types of nodes are considered in the network: the ordinary underwater acoustic sensor nodes and the SN. The underwater acoustic sensor nodes are static after random deployment.
3. The single SN is always the destination node and is located at the center of the surface of the monitoring area, which has a continuous energy supply. However, the energy of the ordinary sensor nodes is restrained and they do not have an energy supply.
4. All nodes (except the SN) have the same initial energy and every node has a unique ID.
5. The locations of the SN and sensor nodes after placement can be obtained by localization algorithms [54], and the distance between two nodes can be calculated.
6. The sending power can be controlled by nodes according to different distances to the receiving nodes.
7. In every small cube, sensor nodes run for CHN. One of them will become the CHN and the others become CMNs. CMNs collect data and send them to the CHN by a single hop. After receiving the data from the CMNs, the CHN processes the data and then transmits them to the SN in one data packet by multiple hops. The relay nodes on multi-hop paths are other CHNs. If some CHNs are near the SN, they can directly forward data to the SN by a single hop.  
Figure 1: The schematic diagram of the network model.
# Suitable Integral Sampling for Bandpass-Sampling Time-Modulated Fourier Transform Spectroscopy
## 2 Principle of BPS-FTS
Consequently, each point of the actual sampled interferogram can be considered as the integral of the product of the ideal interferogram and a rectangular function, where the width of the rectangular function corresponds to the integration time. When the spectrum reconstruction accuracy is not a concern, the rectangular sampling width can assume any value within the sampling interval \(\Delta x\), as determined using Equation (1). However, when considering the spectrum reconstruction accuracy, the sampling width is usually restricted to a reasonable range. Improperly setting the sampling width can result in the distortion of the reconstructed spectrum. Therefore, it is essential to conduct a theoretical analysis to determine an effective integral sampling width that can satisfy the accuracy tolerance for spectrum reconstruction.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
###### Abstract  
Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real-time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5-2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R\({}^{2}\)) of 0.54, mean bias error of -0.75 m/s, mean absolute percent error of 33.20% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data.  
fire spread rate; fire center; fire behavior; Himawari-8; near real-time 2018 10.1654; 10.3390/rs10101654; 13 October 2018; Published: 17 October 2018
# Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data
###### Abstract  
Vertical seismic profiling has garnered widespread attention in the industry as a supplement to seismic exploration due to its higher data quality compared to surface seismic data. However, its unique observation system in which geophones are only distributed within observation wells results in uneven coverage of subsurface structures. This can lead to significant noise when directly applying conventional reverse-time migration techniques used in surface seismic imaging. This study addresses the issue of noise suppression in reverse-time migration imaging associated with walk-away vertical seismic profiling and presents two main innovations. First, a common-receiver reverse-time migration imaging method is proposed, which uses the observation signals as excitation signals for the corresponding shots after reverse-time processing. Second, an excitation-time-constrained cross-correlation imaging condition is introduced to eliminate non-contributing portions of the wavefield, thereby modifying the traditional cross-correlation imaging condition to include an excitation time constraint. The combination of these methods enhances imaging quality by effectively suppressing noise, as demonstrated through theoretical analysis and numerical simulations with synthetic models.  
Keywords:reverse-time migration; seismic data processing; image condition; common-receiver imaging method +
Footnote †: journal: Applied
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 3 Antenna Selection Schemes for FGSM
### ML Methods  
This section examines four different supervised ML methods utilized for TAS.
## 2 Study Area and Data
### Remote Sensing Data  
The C-SAR/01 satellite of China's space infrastructure strategy was launched on 23 November 2021, employing a payload of C-band SAR with 5.4 GHz center frequency and 12 imaging modes, such as spot beam, strip, and TOPSAR (Table 1) [13], which can acquire SAR images with a resolution of 1-500 m and swath of 10-650 km, from single to full polarization, to realize the monitoring of ocean and land resources.  
The C-SAR/01 data used in this experiment are shown in Table 2 and both are single-look complex (SLC) images at Level-1A. The first scene data were acquired on 12 May 2022, in ultra-fine strip (UFS) mode HH polarization with a resolution of 3 m. The second image data were acquired on 15 May in the same year, using fine strip I (FSI) mode HH/HV dual-polarization with a resolution of 5 m. Seven automatic CRs were deployed on the site for synchronous measurements during the satellite imaging, and their distribution is shown in Figure 1 as red triangles, and the orange and red lines in Figure 1 represent the coverage areas of data acquired on 12 May and 15 May, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Imaging Mode** & **Nominal Resolution /m** & **Imaging Swath /km** & **Polarization Mode** \\ \hline \multicolumn{2}{c}{Spotlight (SL)} & 1 & 10 & Single \\ \hline \multirow{5}{*}{Strip} & Ultra-Fine Strip (UFS) & 3 & 30 & Single \\  & Fine Strip I (FSI) & 5 & 50 & Dual \\  & Fine Strip II (FSII) & 10 & 100 & Dual \\  & Standard Strip (SS) & 25 & 130 & Dual \\  & Quad Polarization Strip I & 8 & 30 & Full (Quad) \\  & (QPSI) & 25 & 40 & Full (Quad) \\  & & Strip II (QPSII) & 25 & 40 & Full (Quad) \\ \hline \multirow{5}{*}{Scan} & Narrow ScanSAR (NSC) & 50 & 300 & Dual \\  & Wide ScanSAR (WSC) & 100 & 500 & Dual \\ \cline{1-1}  & Global Observation & 500 & 650 & Dual \\ \cline{1-1}  & (GLO) & & & \\ \hline \multicolumn{2}{c}{Wave (WAV)} & 8 & 20 & Full (Quad) \\ \hline Expanded incidence & Low Incidence & 25 & 130 & Dual \\ angle (EXT) & High Incidence & 25 & 80 & Dual \\ \hline \hline \end{tabular}
\end{table}
Table 1: C-SAR/01 imaging mode.  
Figure 1: Experimental scope and the coverage of two C-SAR/01 images. The yellow, orange, and red lines represent the coverage areas of the Xilinhot Climate Observatory, the image acquired on 12 May, and the image acquired on 15 May. The red triangles represent CRs.
## 3 Probing the Metal-Support Interactions at the Interface
### Other Techniques: Electrochemical and Adsorption Properties Characterization  
The electronic promotional effects originating from metal-support interactions can result in an enhanced electrocatalytic activity of the supported metal species. A useful parameter that can be used as indicator of the electrocatalytic activity is the electrochemical surface area (ECSA). ECSA can be calculated from the charges of H\({}_{2}\) adsorption/desorption peaks in cyclic voltammetry (CV) curves, according to the equation:  
\[\text{ECSA}=\frac{Q_{H}}{M\cdot Q_{ref}}\]  
where \(Q_{H}\) is the amount of charge for the electrodeposition of hydrogen atoms on the metal surface, \(M\) is the mass of metal, and \(Q_{ref}\) is the metal surface density [97].  
Pt-based nanoparticles dispersed on several N-doped carbon surfaces have been demonstrated to possess a higher ECSA compared to the Pt nanoparticles supported on undoped carbon surfaces. This effect could be associated with the enhanced adsorption of protons and electrolyte ions in the electrical double layer, due to the reinforcement in \(\pi\) bonding and higher wettability at the electrode/electrolyte interface associated with nitrogen species.  
Figure 8: Aberration-corrected TEM images for the (**a**) Pd/PS (where PS are pyrolytically stripped carbon nanofibers containing defects and O-functionalities) and (**b**) Pd/HHT (where HHT are carbon nanofiber heat-treated to high temperatures, thus possessing graphitic surface) catalysts provide an atomic resolution view of the Pd nanoparticles (blue) and their interface with the carbon support (red). The insets show the fast Fourier transform results and the particle size distribution. The scale bars in the micrographs (**a**,**b**) represent 5 nm. Reproduced with permission from reference [69]. Copyright 2017.  
While the electrocatalytic activity is determined by ECSA, the catalytic activity is correlated to the adsorption properties of metal surfaces. CO and H\({}_{2}\) are classical probe molecules that are usually adsorbed on metal surfaces to determine their binding properties. High-pressure infrared (HP-FTIR) spectroscopy, as well as attenuated total reflection (ATR) spectroscopy, have been used to monitor the adsorption of probe gases on the surface of metal supported on doped surfaces.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 6 Experiments and Numerical Results
### Effect of Congestion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & & & \(\alpha\) = 0.2 & & & & & & & \(\alpha\) = 0.5 & & & \\ \hline \multirow{4}{*}{**0.2**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} \\ \cline{4-4} \cline{6-14}  & & & & & & & & & & & & & & & & \\ \cline{1-1} \cline{6-14}  & & & & & & & & & & & & & & & & \\ \cline{1-1} \cline{6-14}  & & & & & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{**0.2**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} \\ \cline{1-1} \cline{6-14}  & & & & & & & & & & & & & & & & \\ \cline{1-1} \cline{6-14}  & & & & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{**0.2**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} & \multirow{4}{*}{**0.3**} \\ \cline{1-1} \cline{6-14}  & & & & & & & & & & & & & & & & \\ \cline{1-1} \cline{6-14}  & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Benders decomposition approach vs. CPLEX. leaving a small number of hub locations, which is not favored for the model since inter-hub transportation is well preferred.
## 5 Performance Analysis
### Success Ratio  
In this section we undertake an experimental evaluation of our algorithm P_FENP_MC by comparing it to another known scheduling method, P-EDF-VD. For the latter, task partitioning is done with regard to Condition (14) under the First-Fit Decreasing (FFD) [32] heuristic with sorting as the period. A non-preemptive version of the EDF-VD method is used. For P_FENP_MC, task mapping is done according to the heuristic described in Section 4.  
The parameters used in generating the task sets are provided in the graph caption. Each datapoint was determined by randomly generating 100 task sets. In Figure 8, the task set utilization bound (\(x\)-axis) ranges from 0.2 to 0.8 times the number of processors divided by 2, in steps of 0.1, while in Figure 9, the number of processors (\(x\)-axis) ranges from 2 to 10 in steps of 2. The number of tasks in a task set will vary according to the task set utilization bound, being at least 3 times and at most 9 times the utilization bound. Thus, a higher value on the \(x\)-axis increases the number of tasks in a task set, while a lower value decreases it.  
Figure 8: Success ratio by varying the utilization bound. \(U_{L}=0.05\), \(U_{U}=0.75\), \(Z_{L}=1\), \(Z_{U}=4\).  
As the number of processors increases (see Figure 9), tasks are better scheduled in terms of success ratio when using our proposed algorithm. With more available resources there is a higher chance each task is partitioned on a suitable processor with regard to Conditions I and II (see Section 4.1). The FFD does not run a schedulability test when mapping each task; therefore, if a high number of tasks are partitioned on a single processor, the local scheduling algorithm may return a negative schedulability test.
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 5 Discussion
One approach to apply quality control for such data is to observe trends along overlapping tracks within a given spatial boundary around the hurricane.
# Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning
## 4 Inverse Kinematics
### Denavit-Hartenberg Parameters and Inverse Kinematics Solving  
The Franka Emika Panda robot is a 7-DOF manipulator. Each joint is associated with a DH parameter set \((\theta,d,a,\alpha)\), which defines the transformations between consecutive links. The DH parameters for [PERSON]'s Panda are summarized in Table 1.  
**The forward kinematics** of the manipulator is obtained by multiplying the homogeneous transformation matrices of each link, derived from the DH parameters. The transformation matrix \(A_{i}\) for the \(i\)th joint is given by the following:  
\[A_{i}=\begin{bmatrix}\cos\theta_{i}&-\sin\theta_{i}\cos\alpha_{i}&\sin\theta _{i}\sin\alpha_{i}&a_{i}\cos\theta_{i}\\ \sin\theta_{i}&\cos\theta_{i}\cos\alpha_{i}&-\cos\theta_{i}\sin\alpha_{i}&a_{ i}\sin\theta_{i}\\ 0&\sin\alpha_{i}&\cos\alpha_{i}&d_{i}\\ 0&0&0&1\end{bmatrix}. \tag{21}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Joint \(i\)** & \(d_{i}\) **(m)** & \(a_{i}\) **(m)** & \(a_{i}\) **(rad)** & \(\theta_{i}\) **(rad)** \\ \hline
1 & 0.333 & 0 & 0 & 1.157 \\
2 & 0 & 0 & \(-\pi/2\) & \(-1.066\) \\
3 & 0.316 & 0 & \(\pi/2\) & \(-0.155\) \\
4 & 0 & 0.0825 & \(\pi/2\) & \(-2.239\) \\
5 & 0.384 & \(-0.0825\) & \(-\pi/2\) & \(-1.841\) \\
6 & 0 & 0 & \(\pi/2\) & \(1.003\) \\
7 & 0 & 0.088 & 0 & \(0.469\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: DH Parameters for the Franka Emika Panda.  
The overall transformation from the base frame to the end-effector frame is as follows:  
\[T_{0}^{7}=A_{1}A_{2}A_{3}A_{4}A_{5}A_{6}A_{7}. \tag{22}\]  
**The inverse kinematics** involve finding the joint angles \(\theta_{i}\) given the desired position and orientation of the end effector. This is typically a more complex problem than forward kinematics due to nonlinearity and requires iterative numerical methods or analytical solutions. For the Franka robot, we used an iterative approach based on the Jacobian pseudoinverse method [44; 45]. The goal is to minimize the error between the current end-effector position/orientation and the desired position/orientation. The update rule for the joint angles is given by the following:  
\[\Delta\theta=J^{+}(x_{d}-x), \tag{23}\]  
where \(\Delta\theta\) is the change in joint angle, \(J^{+}\) is the pseudoinverse of the Jacobian matrix \(J\), \(x_{d}\) is the desired end-effector position/orientation, and \(x\) is the current end-effector position/orientation.
# Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals
## 2 Envelope Tracking PA Linearization
### Supply Envelope Generation and ET PA Nonlinear Behavior
The SR process is applied on top of the detroughed envelope with a given configuration of \(OP=20\%\) and \(p=1\), corresponding to an original ACPR around \(-24\) dBc and drain power efficiency around \(16\%\). As observed, the ACPR improves when increasing the number of steps \(\mathbb{N}\). The drain power efficiency, however, is only maintained for \(\mathbb{N}<20\) and then decreases when increasing \(\mathbb{N}\). A good trade-off can be found for \(\mathbb{N}=30\), where the drain efficiency is around \(15.9\%\) and the ACPR is around \(-30\) dBc. Figure 4 shows the scatter plot of the drain power efficiency and ACPR for both the detroughed envelope and the SR reduced envelope, using all the searching test results from Figures 2 and 3. Therefore, Figure 4 shows the power efficiency and linearity trade-off regardless of the envelope generation configuration. Comparing the results obtained with the SR reduced envelope and the detroughed envelope, when the power efficiency is similar (e.g., power efficiency around \(16\%\)), the ACPR performance using the detroughed envelope is only \(-25\) dBc. Instead, by using the SR reduced envelope, it is possible to reach \(-30\) dBc of ACPR. Therefore, with the SR reduction algorithm, it is possible to improve the ET PA linearity at the price of degrading the power efficiency. As observed, the number of steps \(\mathbb{N}\) can be properly chosen to avoid a significant degradation of the drain power efficiency. However, even after SR reduction of the supply envelope, the ACPR requirement of \(-36\) dBc is still not met, and thus further linearity enhancement is required. As discussed in [19], another source of nonlinear distortion of the ET PA system is the RF to envelope leakage. Due to the intrinsic design of the ETM, the ETM output impedance is not a perfectly constant low value. As a consequence, the distortion at the RF PA output is leaked into the envelope supply path (i.e., the output port of the ETM). This leakage is combined with the supply envelope, causing nonlinear distortion at the RF PA output. Figure 5 shows the spectra of the supply envelope at the ETM output when considering the amplification of an SR reduced envelope (\(OP=0.2,p=1,\mathbb{N}=30\)). When there is no RF input signal, the ETM can amplify the SR reduced envelope up to its bandwidth limitation. Instead, when the RF input is turned on, some RF leakage is added to the ETM output spectrum, as observed in Figure 5. In the latter case, the spectrum of the amplified supply envelope presents a similar shape to the spectra of the original envelope of the baseband input \(|u_{BB}|\)  
Figure 3: ACPR and efficiency performance of the ET PA with the NR-60 MHz OFDM-like signal, using the SR envelope with p = 1, OP = 0.2, and different \(\mathbb{N}\) (number of steps) configurations. The envelope linearization method proposed in this paper to meet the required linearity specifications is described in the following subsection.
# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Biological Features
They demonstrated how two autonomous gliders could track an evolving plume [49]: one glider tracked the boundary of the plume, while the other glider tracked the centroid of the plume. A zig-zag in the tranquil ocean path-planner [50] was presented, combining two algorithms developed for autonomous underwater gliders for a long-term monitoring of harmful algal blooms. The first algorithm, the path-planning algorithm (zig-zag), was to pass through the area of interest, while avoiding the strong current areas. The second was the pitch angle algorithm (saw-tooth), to secure a high sampling resolution at the region of survey interest. They divided the domain into sub-sections like a checkerboard. Formulating a reward function, based on a Bellman-Ford algorithm [51], the waypoints were adaptively generated for the optimal path that is the shortest trajectory to visit several designated high-interest regions. The average magnitude of the ocean current was extracted using the ROMS data. Finally, the path connecting each candidate waypoint with minimum cost under a set time constraint was generated. The pitch angle of the vehicle governs the resolution--the density of sampling. It was adjusted by altering between 15\({}^{\circ}\)-35\({}^{\circ}\). Over the iteration process, the two algorithms continuously seek the shortest path by adjusting the pitch angle. [PERSON] et al. [52] presented an opportunistic sampling method for an AUV with the aim to retrieve relevant water samples for ex-situ analysis. Three submodular solutions, a probabilistic model, Bayesian optimisation and optimal stopping theory, formed their method. The relevant environmental correlation with the geographic parameters from the predictive models, was used to detect the biological event. Firstly, the probabilistic model on-board the vehicle generates predictions of the mean plankton abundance in real time through mapping the correlation of the physical parameters (temperature and salinity) with the plankton abundance. Then, the second Bayesian optimisation module designs a sampling policy of candidate samples addressing the highest utility (best sampling location). Finally, the optimal stopping theory considers the issues involved with triggering a desired action (sampling) due to issues associated with any limitation in the number of sampling devices. In order to secure the optimal sampling results, a submodular secretary algorithm was used [53]. It pursues the top sum of the individual rating per candidate via capturing the sum of organism abundance observed to select the best candidate among the selected group. Their work improved AUV-based knowledge extraction in monitoring biological activity of marine algae. In summary, the advent of biological features tends to take place repeatedly and it is highly correlated with the physical characteristics of the ocean. Therefore, ocean-model-based approaches based on cumulative ocean databases could be used as the world model for an AUV to adaptively track biological features.