esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Simulations
These joint variables determine the configuration of the robot in the meridian plane and in particular the location of the wrist (the origin of coordinate system 4 in Figure 2). Therefore, a further numerical analysis was carried out to study the dependence of the first three natural frequencies on the position of the wrist in the meridian plane. The results are presented in terms of contour plots, in which the darker colors represent the lower frequencies and the lighter colors represent the higher frequencies. Since some locations can be reached with two robot configurations (elbow-up and elbow-down), both configurations are considered in separate plots. Figure 14 shows that the first natural frequency decreases in a regular way when the distance between the wrist and the robot base increases, and this effect takes place both in the elbow-up and in the elbow-down configuration. Figure 13: Mean values and standard deviations of modal damping in the workspace, (**a**) random variations in J2, J3, J4, J5, (**b**) random variations in J2 and J3. The contour plot of the second natural frequency is more complex (Figure 15). The second natural frequency reaches the lowest values when the arm is extended in order to reach locations in front of the base or on the back of the base. In these configurations, links 2 and 3 tend to align. The maximum value of the second natural frequency takes place when the arm has to reach locations above the base. This effect is present in both the considered configurations. Figure 16 shows a rather regular trend of the third natural frequency. In both configurations, this natural frequency tends to increase when the wrist location is far from the base and links 2 and 3 tend to align (joint variable J3 is close to 90\({}^{\circ}\)). Figure 14: Contour plot of the natural frequency of mode 1 in the meridian plane, (**a**) elbow-up, (**b**) elbow-down. Figure 15: Contour plot of the natural frequency of mode 2 in the meridian plane, (**a**) elbow-up, (**b**) elbow-down. Therefore, the angle (\(q_{3}\)) between link 2 and 3 has opposite effects on the second and third natural frequencies that usually correspond to modes that take place in the meridian plane. The second natural frequency decreases when the two links tend to align, because the direct inertia term \(M_{22}\) increases. The third natural frequency increases when the two links tend to align, because the inertial cross coupling term (\(M_{23}\)) between J2 and J3 increases (see Figure 5). This effect takes place even if a simple 2 DOF model of the robot in the meridian plane is adopted [28]. Finally, the analysis of Figures 15 and 16 shows that it is rather difficult to maximize both the second and the third natural frequencies. Only in a limited region of the workspace above the robot it is possible to achieve large values of both frequencies.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 6 LEAP  
### Mission Overview  
The Large E Area burst Polarimeter ([[https://sti.usra.edu/in-the-news/leap-mission-study/](https://sti.usra.edu/in-the-news/leap-mission-study/)]([https://sti.usra.edu/in-the-news/leap-mission-study/](https://sti.usra.edu/in-the-news/leap-mission-study/)) (LEAP) is a mission that was proposed in 2021 to NASA's Astrophysics Mission of Opportunity Program. The LEAP instrument is a wide-FoV Compton polarimeter that measures GRB polarization over the energy range from 50 keV to 1 MeV and performs GRB spectroscopy from 20 keV to 5 MeV. If approved (a final decision on the selection is expected during the first quarter of 2024), it will be deployed as an external payload on the International Space Station (ISS) in 2027 for a three-year mission [49; 50]. The LEAP science investigation is based on the ability to distinguish between three classes of GRB models [51]. The baseline science investigation requires the observation of 65 GRBs with a minimum detectable polarization (MDP) of 30% or better. Evidence of polarized \(\gamma\)-ray emission in GRBs (\(>\)100 keV) has been accumulated in recent years, but the limited sensitivity of these measurements does not yet yield a clear picture of the underlying physics [52; 53; 54]. A sensitive and systematic study of GRB polarization, such as the one that will be provided by LEAP, is needed to remedy this situation.
## 3 Results
### Data Preparation for LSTM Model  
The occupancy dataset preparation for the LSTM model must be framed as a supervised learning problem. The data are multivariate data that require input variables to be normalized [43]. The data set is composed of multiple variables of different units. The reason for the re-scaling technique to be used as a normalization technique to stretch and squeeze the values in the datasets to fit on a scale from 0 to 1. The model setup is considered to predict the occupancy at the next hours from the previous measurements at the prior time steps of five input variables used [temperature, humidity, light, occupancy, and CO2] in the experiment. The dataset is split into two parts that are train and test sets with 70% and 30% respectively. Then the train and test are split into input and output variables. The LSTM model expects 3D dimension variables namely [samples, timesteps, features] reshaped from input variables. Different configurations are done progressively, which is to define the first hidden layer with 1 neuron for predicting occupancy. The input shape will be a 3-times step with 5 features. The efficient Adam version of stochastic gradient descent is used with the Mean Squared Error (MSE) loss function. This research used 50 training epochs with a batch size of 64 of four iterations for a better fit model. The authors explored the appropriateness justification of deep learning towards the usage of automatic feature extraction. The adoption of real-time experimental data is something that was inspired to adopt in this research future work [44]. The importance of multivariate or multimodal data has been explored [45]. The authors register the point that most research articles have still explored time sequence data mostly. So, this paper with the application area of air pollution prediction has explored generative adversarial networks along with data augmentation through raw images to overcome the class imbalance. This has shown considerable improvement in classification accuracy.
# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 4 Results
### Shape of DropBlock_Diagonal  
In this section, we demonstrate the effectiveness of DropBlock_Diagonal through extensive experiments. Figure 9a shows the change in accuracy as _keep_prob_ increases when different shapes of DropBlock_Diagonal are added to the network. Figure 9b shows the change in F1 as _keep_prob_ increases when different shapes of DropBlock_Diagonal are added to the network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc/\%** & **Se/\%** & **Sp/\%** & **F1/\%** \\ \hline r = 8 & 97.12 & **82.69** & 98.46 & 82.91 \\ \hline r = 16 & 97.12 & 82.13 & 98.52 & 82.85 \\ \hline r = 32 & **97.14** & 82.17 & **98.53** & **82.93** \\ \hline \hline \end{tabular} The bold font indicates the optimal value.  
\end{table}
Table 2: Impact of different reduction rates on network models.  
Figure 8: DropBlock_Diagonal location discussion.  
Here, Dxy represents DropBlock_Diagonal, where x represents the value of _diag_1_, and y represents the value of _num_diag_. From Figure 8(a), it can be observed that the network segmentation accuracy is generally higher when _keep_prob_ is between 0.94 and 0.99. From Figure 8(b), it can be seen that the network's F1 is generally higher when _keep_prob_ is between 0.91 and 0.96. Considering both Acc and F1, the network performs best when _keep_prob_ is set to 0.94-0.96. In this study, D70 is selected as the DropBlock_Diagonal shape for the DRIVE dataset with _keep_prob_ set to 0.96, and for the CHASE_DB1 dataset with _keep_prob_ set to 0.94. When the network incorporates D70, the model loss changes as shown in Table 3. After the addition of D70, Train_Loss increases while Val_Loss decreases, indicating that the inclusion of D70 improves the network's overfitting situation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Train\_Loss** & **Val\_Loss** \\ \hline D70 & 0.31542 & 1.48000 \\ \hline Without D70 & 0.31251 & 1.48030 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The model’s loss changes when the network incorporates D70.  
Figure 9: DropBlock_Diagonal shape discussion.  
It is also observed that the network's performance is generally better than when the network does not include DropBlock_Diagonal (when _keep_prob_ = 1). DropBlock_Diagonal, by setting its dropout regions to diagonals of different widths, can make the network more sensitive to vessel information and better learn vessel features.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Altimeter  
OWH measured by altimeter is affected by various static or dynamic characteristics, such as gravity, tides, ocean currents, El Nino, and the dynamics of basin circulation. Since the mountains on the seafloor add extra pull to Earth's gravity field and draw more water around them, the effect of ocean bottom topography on OWH is 100 times greater than other oceanographic signals [323]. Moreover, a bathymetric signal is expressed in a shorter-spatial scale than the other oceanographic signals and, therefore, is readily distinguished [324]. These facts make altimeters promising tools for globally uniform reconnaissance of deep-sea bottom topography. Estimating the shape and structure of the ocean bottom using altimeters is called altimetric bathymetry [325].  
To investigate the performance of altimetric bathymetry in retrieving topographic profiles, [PERSON] et al. (2018) [326] compared the bathymetry from four altimetry missions (European Remote Sensing 2 (ERS-2), ENVISAT, Satellite with ARgos and ALtiKa (SARAL), and CryoSat-2) with the LiDAR-derived topography over the intertidal area of the Arcachon Bay. They found that SARAL and CryoSat-2 with a Root Mean Square Error (RMSE) of lower than 0.44 m provided more accurate bathymetric maps than LiDAR.  
Altimetric bathymetry has many scientific and commercial applications, such as numerical ocean modeling, fisheries, and locating obstructions/constrictions to the major ocean currents. However, they do not have sufficient accuracy and resolution for assessing navigational hazards [327].
## 4 Machine Learning Algorithms
### Data Preprocessing
#### 4.1.3 Data Normalization  
Since logging curves of different parameters exhibit varied magnitudes, direct input into the training model would significantly distort TOC prediction results. To eliminate the influence of dimensional disparities, the sample dataset undergoes normalization, mapping input curve values to the range [0, 1], followed by a 7:3 split into training and test sets.  
Figure 6: Data calibration diagram.  
The training set is utilized for model development, while the test set evaluates model performance.  
\[X_{N}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{4}\]  
where \(X_{N}\) represents normalized value. \(X\) is the original sample data. _Xmax_, _Xmin_ are the maximum and minimum values of sample dataset.
## 3 Experimental and Simulation Setup  
The PS-CAP VLC experimental setup is shown in Figure 1. Firstly, two data streams are loaded by the CCDM. After LDPC encoding and QAM mapping, the symbols are upsampled 20 times. Then the real part and imaginary part of the signal are filtered by the Nyquist filter pairs, respectively. Afterward, the two signals add up and become a single signal, and are converted from a digital signal to an analog signal by the arbitrary waveform generator (AWG). After the equalization of a T-bridge hardware equalizer (Eq.) and amplification of the electrical amplifier (EA), the signal is coupled with DC and converted from an electric signal to an optical signal by the blue LED. After transmission in a 1.2 m water tank, the optical signal is collected and transferred to the current signal by a PIN (s10784). Then the current signal is converted to a voltage signal by a trans-impedance amplifier (TIA). After being amplified by an electrical amplifier, the signal is collected by an oscilloscope and loaded by the CAP demodulation offline processing block. For more details of CAP, please refer to the article [3]. Subsequently, the matched filter pairs, which are the corresponding shaping filters of Nyquist filter pairs, filter and separate the received signal into in-phase and quadrature signals. Then the signal is compensated by an LMS equalizer with the taps number of 43. The taps number is optimized and will not change throughout the experiment. After QAM demodulation, LDPC decoding, and the decoding of Inverse CCDM, the signal is recovered to bitstream again. The detailed parameters of Figure 1 are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Hardware Parameters**} & \multicolumn{2}{c}{**Software Parameters**} \\ \hline
**Terms** & **Parameters** & **Terms** & **Parameters** \\ \hline AWG & AWG 710B & Roll-off of Nyquist filters & 0.205 \\ OSC & HP85545A & AWG sample rate & 400 MHz \\ EA & ZHL-6A-S+ & OSC sample rate & 1 GHz \\ Bias-Tee & ZFBT-4R2 GW-FT+ & Upsample times & 20 \\ TIA & MAX3665 & LMS taps & 43 \\ LED & Special silicon LED & LDPC type & DVB-S2 \\ PIN & Hamamatsu S10784 & Simulation fading channel factor \(f_{0}\) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed parameters of experimental and simulation setup.  
Figure 1: Experimental and simulation setup.  
For simulation, the PS modulation block, CAP modulation block, CAP demodulation block, and PS demodulation block remain unchanged, while the simulation VLC channel replaces the processing after CAP modulation and before CAP demodulation. The simulation VLC channel is the combination of an exponential fading channel in the frequency domain and additive white Gaussian noise (AWGN).
## 4 Results and Discussion
### Accuracy Assessment  
In order to verify the reliability of the results obtained through two models, we calculated the residual phase for all the coherent points. The average residual phase for each interferogram is shown in Figure 18. The overall magnitude of the residual phase for both cases is less than 1 rad, indicating both the linear model and seasonal model have good accuracy for time series deformation modeling, thus are reliable in the application of deformation monitoring in soft clay subgrade areas. For Lungui Highway, the root mean square (RMS) of the residual phase obtained by seasonal model is 1.8 rad, whereas the linear model is 2.1 rad, with a 14% improvement. For G1501, the mean square root of the residual phase acquired by seasonal model is 2.7 rad, whereas the linear model is 3.4 rad, with a 20% improvement. Both figures reveal that the seasonal model shows better modelling accuracy. Correspondingly, the seasonal model is more highly recommended for test highways in this paper.  
In addition, ground leveling measurements over the period of October 2014 to March 2015 were collected to estimate the external accuracy of our experiment. The location of leveling point K4 is shown in Figure 9. Figure 19 shows the time series results generated by both models at K4 compared to that of leveling. It can be obviously seen that both models show good consistency with the leveling measurements, indicating the SBAS technology is feasible for time series deformation monitoring of highways built on soft clay subgrade. The root mean square error (RMSE) for linear model results is estimated to be \(\pm\)4 mm, whereas, for seasonal model, this is \(\pm\)3 mm. From our quantitative comparison, the accuracy of the seasonal model is slightly higher than that of the linear model. Considering both the accuracy evaluated through the residual phase and the comparison with leveling measurements, the seasonal model is more highly recommended here for time series investigation in our case study.  
Figure 18: The residual phase comparison. (\(x\) axis represents the index of interferograms; \(y\) axis represents the mean residual phase for all coherent points.) (**a**) Lungui Highway; (**b**) G1501 Highway.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 4 Results and Experimentation
### Overall Residual Energy  
Due to the limited battery capacity of the UAVs, it is vital that regulated energy drain is implemented as soon as possible. The energy consumption is measured for a predetermined number of transmissions (transmissions). UAVs rely on energy as their primary source of propulsion. The limited availability of energy resources places significant restrictions on the extensive range of uses for unmanned aerial vehicles (UAVs). Energy is dissipated by three main mechanisms in unmanned aerial vehicles (UAVs): the energy required to operate the UAV, the energy consumed by the various sensors mounted on the UAV, and the energy consumed by the UAV for communication with other unmanned aerial vehicles (which is the primary source of energy consumption). With the help of LEACH, LEACH-C, PSO-HAS, and SEED, we could compute the total energy consumed by the FANET during the 120 s. Figure 9 illustrates how the energy consumption of UAVs in a FANET grows as the number of UAVs in the FANET increases. The lower energy usage of our proposed system is due to the application of energy--conscious CH selection and cluster management techniques. It is undeniable that EE-SS outperforms the other method, which translates into lower energy consumption and a more extended network lifetime. With increasing node distance, energy consumption increases linearly, with the suggested technique growing slower than the previous competing models, indicating more excellent performance.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 2 Synchronization Issues in LoRa
### Analysis of Imperfect Synchronization on Symbol Estimation
#### 2.2.1 Impact of the CFO and the STO on Symbol Estimation  
When the received signal is affected by a CFO, the \(argmax\) of all the FFTs would be shifted by \(\Delta fT=C+\
u\), with  
* \(C=\lfloor\Delta fT\rceil\) is an integer offset that shifts all the symbols;
* \(v\in[-0.5,0.5)\) is the fractional part of the CFO that shifts the spectrum line between two frequency bins, effectively making a \(sinc\) kernel appears in the frequency domain.  
In the presence of the STO, an ISI occurs as depicted in Figure 1, which leads to the emergence of two cardinal sines with positions shifted by \(\frac{\Delta\tau}{t_{s}}=L+\lambda\), where \(\lambda\in[0,1)\) being the fractional STO. In addition, \(\lambda\) may cause a phase discontinuity of the modulated chirps [14], which implies a biased FFT processing.  
For more details on the impact of the CFO and STO, readers can refer to [21; 27].
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 4 Results
### National Statistical Analysis  
For each country, we summarize the total area of the mapped coastal pond aquaculture at national level (see Figure 11, GADN level 0), at state/province/equivalent level (GADM level 1), and district/county/equivalent level (GADM level 2). Statistics on annual aquaculture production are provided by the FAO and are listed individually for all countries. Sub-national production data are partially available for individual countries (e.g., China), but these are not available in standardized form nor accessible via a central platform for all countries at a global level. However, data at the national level may not be suitable nor relevant enough to describe regional distribution of coastal aquaculture. For this reason, we calculated coastal pond area distribution also at the sub-national administrative units to derive statistical information at the regional level. In Figure 12, we illustrate the distribution of pond area at district level for the island state of Indonesia.  
The counting and measuring of the shapes of the mapped aquaculture pond objects provides essential information to quantitatively describe the composition of shapes among different regions and countries and to recognize and describe patterns. Therefore, we analyzed various pond-specific topographic and geometric features (Figure 13) and calculated, for example, the average size and mean compactness (Figure 14) of the mapped aquaculture for each country in the study region.  
For the five countries with the highest shares of mapped aquaculture area, we computed scatterplots of the elevation and distance of all ponds and found that there are different spatial patterns (Figure 13A). In China, aquaculture areas seem to be well distributed within a 150 km distance to the shorelines, while in Indonesia (and to a lesser extent also in Vietnam and India) the ponds are more concentrated within a 10-20 km distance from the coast. The reason for this could be the specific topography of the main islands of the archipelagic country Indonesia, which are characterized by volcanic mountains that slope down to the coastal plains.  
Figure 10: Total area of Earth-observation-derived aquaculture ponds per kilometer distance to the coastline.  
Figure 11: Bar chart: Total area (ha) of the mapped pond aquaculture per country in the study; Pie chart: Share of total mapped aquaculture area (ha) among the top 5 ranking countries (in percent).  
Figure 12: Mapping result: total pond aquaculture area for Indonesia at district level.  
Figure 13: Scatterplots of elevation and distance of aquaculture categorized among different pond sizes for the top 5 Asian countries (China, Indonesia, India, Vietnam, and Bangladesh) (**A**); Boxplots of the area of the convex hull (**B**) and the ratio between the pond area and its convex hull (**C**) for the mapped ponds from the five defined macro-regions East Asia (EA), Northeast Asia (NEA), South Asia (SA), Southeast Asia Mainland (SEAL), and Southeast Asia Maritime (SEAM).