esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Experiment Test Method
#### 3.2.4 Measurement of Sound Absorption Coefficient  
The sound absorption coefficient of the porous materials was measured using the two-microphone transfer function method. The setup consisted of an impedance tube (AWA8551 model), a multi-channel noise analysis tester (AWA6290M model), and pulse analysis software. The measurements were performed at a constant temperature (23 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C). The impedance tube system included two tubes:  
A large tube with a diameter of 100 mm, used for testing absorption coefficients in the frequency range of 50-1600 Hz.  
A small tube with a diameter of 20 mm, used for testing absorption coefficients in the frequency range of 500-6300 Hz.  
The testing method complies with the national standard GB/T 18696.2-2002 [43], \"Acoustics-Determination of sound absorption coefficient and impedance in impedance tubes-Part 2: Transfer-function method\". Figure S2 illustrates the schematic diagram of the sound absorption device.  
Before testing, the AWA6223 sound calibrator (Hangzhou Aihua Instruments Co., Ltd., Hangzhou, China) was used to calibrate the sensors. The 1/3 octave band method was applied to measure the sound absorption coefficients. Three samples were tested for each density, and each sample was measured three times. The average value of these measurements was taken as the sound absorption coefficient for that type of sample. The testing frequency range was 50-6300 Hz.  
The noise reduction coefficient (NRC) was determined according to the American standard ASTM C634-13 (2021) [44]. NRC is calculated as the average of the 1/3 octave band sound absorption coefficients at 250 Hz, 500 Hz, 1000 Hz, and 2000 Hz, using the following formula:  
\[\text{NRC}=\frac{\alpha_{250}+\alpha_{500}+\alpha_{1000}+\alpha_{2000}}{4} \tag{9}\]
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 3 Results
### Experiment 2: Evaluating the Impact of the StartingTree on DTM Pipelines
GTM has an advantage over the other two DTM methods (running time advantage over both, and slight accuracy advantage over Constrained-INC). Hence, the benefit in terms of accuracy in using these pipelines is limited if the dataset is small enough for RAxML to be used, and these pipelines are always slower than IQ-TREE. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Running Time (h)** & **Tree Error** \\ \hline FastTree & 0.06 & 23.9\% \\ IQ-TREE & 2.3 & 19.6\% \\ RAxML & 7.1 & **18.2\%** \\ \hline DTMs, FastTree starting tree & & \\ Constrained-INC & 0.8 & 18.9\% \\ GTM & 0.8 & 18.9\% \\ TreeMerge & 6.5 & 18.9\% \\ \hline DTMs, IQ-TREE starting tree & & \\ Constrained-INC & 3.1 & 19.7\% \\ GTM & 3.1 & 18.7\% \\ TreeMerge & 8.5 & 18.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results on the Cox1-HET datasets. Tree error (FN) rates and running time for existing ML methods and DTM pipelines on the Cox1-HET model condition (averaged across 10 replicates). The DTM pipelines use the starting tree as a guide tree with two options for guide trees (FastTree or IQ-TREE), and then decompose to maximum subset size 500. If a pipeline requires a distance matrix, as is the case with Constrained-INC and TreeMerge, the topological distance matrix is obtained from the starting tree. The most accurate result is boldfaced. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Running Time (h)** & **Tree Error** \\ \hline FastTree & 0.1 & 14.9\% \\ IQ-TREE & 1.0 & 15.1\% \\ RAxML & 24 * & 15.1\% \\ \hline DTMs, FastTree starting tree & & \\ Constrained-INC & 0.4 & 15.1\% \\ GTM & 0.4 & 14.7\% \\ TreeMerge & 1.2 & 14.7\% \\ \hline DTMs, IQ-TREE starting tree & & \\ Constrained-INC & 1.2 & 14.5\% \\ GTM & 1.2 & **14.4\%** \\ TreeMerge & 1.9 & **14.4\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results on the RNASim1000 datasets. Tree error (FN) rates and running time for ML methods and DTM pipelines on the RNASim1000 model condition (averaged across five replicates). The DTM pipelines use the starting tree as the guide tree (with either FastTree or IQ-TREE), and decompose to a maximum subset size 500. If a pipeline requires a distance matrix, as is the case with Constrained-INC and TreeMerge, the topological distance matrix is obtained from the starting tree. The asterisk (*) for RAxML running time indicates that it had not been completed by that time, and we used the best scoring tree it found in that time period. The most accurate results are boldfaced.
## 5 Attenuation Method Experiment
### Phase II: Attenuation of Service Channels
#### 5.2.2 Raman Noise Estimation
* \(P_{\overline{SpRS|30}}=-106.79\text{ dBm}\),
- \(P_{\overline{SpRS|29}}=-105.48\text{ dBm}\),
- \(P_{SpRS|QKD}=-103.08\text{ dBm}\). Figure 4: The figure shows what components of Raman noise produce errors in detecting a quantum signal. The total malicious Raman noise consists of the forward component CH30 and the backward component CH29. - \(P_{\overline{SpRS|30}}=-126.79\) dBm, **
- \(P_{\overline{SpRS|29}}=-125.48\) dBm, **
- \(P_{SpRS|QKD}=-123.08\) dBm. As expected, in both cases, the Raman backscatter is slightly higher than the forward scatter. Overall, however, the difference is minimal and does not change much with increasing attenuation. At this distance, the difference can thus be neglected, and the power of the malicious Raman noise in both directions can be declared to be equal. By summing them together, the power obtained is twice as high (+3 dB).
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 5 Experiment and Discussion  
### Simulation and Analysis  
In order to verify the performance of the factor graph method proposed in this paper, the simulation and analysis are carried out with different methods. In the simulation, a typical flight trajectory has been designed. We assume a MUAV equipped with many sensors, including IMU, GPS receiver, magnetometer, and barometer. IMU sensors produce measurements at a high rate while other sensors generate measurements at lower rates. The flight time is 600 s, and the initial position is [106.5\({}^{\circ}\), 29.53\({}^{\circ}\), 25 m]. Parameters of navigation sensors are shown in Table 1.  
The extended Kalman filter (EKF) has been used widely in integrated navigation systems, which linearizes all nonlinear models. In the traditional EKF algorithm, the state propagates with the IMU update rate, and performs a measurement update whenever the measurements are available. The factor graph method is compared with traditional EKF method in the simulation. The flight track is shown in Figure 8. The five-pointed star represents the starting point. The flight altitude remains the same.  
Monte-Carlo simulations have been performed and the RMSE performances are used to compare the accuracy of different algorithms. After 100 Monte Carlo simulations, the average RMSE of both methods are given in Table 2. Compared with the ground truth data from the flight track, Figure 9a,b illustrates the average RMSE of the position error and the velocity error generated by these two algorithms, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Error** & **Value** & **Frequency** \\ \hline \multirow{4}{*}{IMU} & Gyro constant drift & 10\({}^{\circ}\)/h & \multirow{4}{*}{50 Hz} \\  & Gyro first-order Markov process & 10\({}^{\circ}\)/h & \\  & Gyro white noise measurement & 10\({}^{\circ}\)/h & \\  & Accelerometer first-order Markov process & 1 \(\times\) 10\({}^{-4}\) g & \\ \hline \multirow{2}{*}{GPS} & Position error noise & 10 m, 10 m, 20 m & \multirow{2}{*}{1 Hz} \\  & Velocity error noise & 0.1 m/s, 0.1 m/s, 0.1 m/s & \\ \hline Magnetometer & Heading error noise & 0.2\({}^{\circ}\) & 20 Hz \\ \hline Barometer & Height error noise & 5 m & 10 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of navigation sensors.  
From the above simulation results, the average RMSE performances of the EKF and the factor graph filter are compared in Table 2. The improvement of the factor graph filter is obvious. The corresponding accuracy in the velocity and position of the factor graph method improve as well.  
Figure 8: Flight track of the MUAV in the simulation.  
Figure 9: Comparison curves of two filter methods. (**a**) Position error contrast curves; (**b**) Velocity error contrast curves.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 8 Undistorted Airy Pulses  
### Undistorted Ideal Accelerating Pulses  
One of the interesting properties of the dispersion equation is that the Airy function is its accelerating solution [32; 33; 34]. However, despite dispersion, the shape of this accelerating solution remains intact during propagation. This property can be useful in optical communications where the encoded data propagates in the dispersive medium without experiencing distortions.  
That is, if the initial pulse is an Airy function pulse:  
\[A(t,z)=A_{0}\text{Ai}(t/\theta) \tag{87}\]  
where Ai\((\tau)\) stands for the Airy function [35], then the pulse at the end of the dispersive medium keeps its initial profile despite being accelerated; after a distance \(z\), it reads:  
\[A(t,z)=A_{0}Ai\left[t/\theta-\left(\beta_{2}z/2\theta^{2}\right)^{2}\right] \exp\left[-it\beta_{2}z/2\theta^{3}+i\left(\beta_{2}z/\theta^{2}\right)^{3}/12\right] \tag{88}\]  
In Figure 25, the dynamics of this pulse is presented for different distances.  
As can be seen in Figure 25, the pulse's amplitude shape \(|A(t,z)/A_{0}|=Ai\left[t/\theta-\left(\beta_{2}z/2\theta^{2}\right)^{2}\right]\) remains intact, i.e., it keeps the same Airy shape. The only difference is that the pulse's temporal coordinate \(\left[t/\theta\right]\) has shifted with the distance to \(t/\theta-\left(\beta_{2}z/2\theta^{2}\right)^{2}\), and therefore the pulse's temporal location accelerates according to the equation  
\[t=(\beta_{2}z)^{2}/4\theta^{3}. \tag{89}\]  
Therefore, the square of the dispersion coefficient is proportional to the pulse's \"acceleration\".  
Figure 24: Same as Figure 22, but for Equation (86) with \(\delta=0.6/\theta\).
## 2 Materials and Methods
### Model Description and Improvement
#### 2.1.1 Dynamic Vegetation Option  
The dynamic vegetation option in Noah-MP describes the terrestrial carbon budget in relation to canopy photosynthesis, respiration, and carbon allocation to roots, stems, leaves, and wood. The default version allocates assimilated carbon preferentially to leaves, stems, and wood, with the remainder allocated to roots. However, the UA version modifies this allocation scheme by prioritizing root systems to meet transpiration demands [26]:  
\[f_{root}=0.3(1-\beta) \tag{1}\]  
\[f_{leaf}=(1-f_{root})\times 10000\times\frac{e^{-2\times LAI}}{1+10000\times e ^{-2\times LAI}} \tag{2}\]  
\[f_{stem}=\left(1-f_{root}-f_{leaf}\right)\times 0.2 \tag{3}\]\[f_{\text{\it{twoord}}}=1-f_{\text{\it{root}}}-f_{\text{\it{leaf}}}-f_{\text{\it{ stem}}} \tag{4}\]  
where \(f_{\text{\it{root}}},f_{\text{\it{leaf}}},f_{\text{\it{stem}}}\), and \(f_{\text{\it{broad}}}\) are the fraction of the carbon flux that goes into roots, leaves, stems, and woods, \(\beta\) is the water stress factor for canopy photosynthesis and stomatal conductance, and \(LAI\) is the leaf area index.  
At each time step (\(\Delta t\)), net primary production (\(NPP\)) is determined as the difference between gross primary production (\(GPP\)) and plant respiration. The turnover rate of all plant tissues (\(Ovt\)) and the dying rate of seasonal leaves and stems (\(Death\)) are calculated using experience formulas [17]. The plant carbon mass (\(m\)) is updated at the end of each time step as follows:  
\[m=m+(NPP-Ovt-Death)\times\Delta t \tag{5}\]  
Based on the updated carbon mass of leaves (\(m_{\text{\it{leaf}}}\)) and stems (\(m_{\text{\it{stem}}}\)), the \(LAI\) and stem area index (\(SAI\)) are updated:  
\[LAI =m_{\text{\it{leaf}}}\times LAPM \tag{6}\] \[SAI =m_{\text{\it{stem}}}\times SAPM \tag{7}\]  
where \(LAPM\) and \(SAPM\) are the leaf and stem area per unit mass (m\({}^{2}\) g\({}^{-1}\)).
## 3 Results  
### Validation Experiment  
To assess the applicability of two feature extraction networks within the PSPNet model for search and rescue scenarios, comparative experiments were conducted. Performance evaluation was performed on the test set of a self-built search and rescue dataset for both PSPNet configurations. The primary evaluation metrics include IoU, accuracy, total parameters, total GFLOPS, memory usage (CPU, GPU, TPU), and model size. The results are presented in Table 2.  
The experimental results show that replacing the backbone of PSPNet with MobileNetV2 yields an IoU of 79.74% and an accuracy of 96.07% on the test set, which are slightly lower than the baseline model. However, this modification significantly reduces the number of floating-point operations and the model parameter count by several orders of magnitude. Additionally, the memory usage is substantially decreased on CPU, GPU, and TPU by 7.14 GB, 6.69 GB, and 2.15 GB, respectively. These improvements markedly enhance the model's computational efficiency and lightweight nature, offering clear practical advantages for deployment on resource-constrained devices in complex environments. Therefore, replacing the backbone with MobileNetV2 proves to be both a reasonable and effective choice.  
To enable semantic segmentation for individuals in search and rescue scenarios under limited hardware conditions, the PSPNet architecture was further optimized by selecting lightweight backbone networks. The comparison focused on ShuffleNetV1, ShuffleNetV2, MobileNetV1, MobileNetV2, and MobileNetV3, with experiments conducted on a self-built dataset.  
As shown in Table 3, MobileNetV2 performs the best when used as the backbone network. It also has the smallest model parameter size among the options, making it suitable for conditions with limited resources.
# MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
## 3 Experiment
### Evaluation Indicators  
For retinal blood vessel image segmentation, the pixels in the image were actually divided into a blood vessel image and a background image. In order to make a qualitative evaluation of the experimental results of this article, we used several general retinal vessel segmentation performance evaluation indicators:  
* Sensitivity (SE): The ratio of the total number of correctly segmented blood vessel pixels to the total number of manually segmented blood vessel pixels.
* Accuracy (ACC): The ratio of the total number of correctly segmented blood vessels and background pixels to the pixels of the entire image.
* precision: The ratio of the actual blood vessel pixels in the segmented blood vessel pixels.
* F1 value: The result of combining sensitivity and precision. When the F1 value is high, the method is more effective.
* Area Under Curve (AUC): The area under the receiver operating characteristic (ROC) curve. The larger the value, the better the segmentation effect. \[Sensitivity=\frac{T_{P}}{T_{P}+F_{N}}\] (5) \[Accuracy=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}}\] (6) \[Precision=\frac{T_{P}}{T_{P}+F_{P}}\] (7) \[F1=2\times\frac{Precision\times Sensitivity}{Precision+Sensitivity}\] (8) Among them, \(T_{P}\), \(T_{N}\), \(F_{P}\), and \(F_{N}\) respectively represent true positive, true negative, false positive, and false negative. \[Jaccard=\frac{\mid G\cap P\mid}{\mid G\cup P\mid}\] (9) where \(G\) is the set of ground truth pixels and \(P\) is the set of predicted pixels.
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## 3 Results
### Data Acquisition
#### 3.1.1 Calibration Data Set Acquisition and Control Points Set Selection  
According to the data acquisition method mentioned in Section 2.2.2, the calibration data set containing 358 points is acquired and the result is shown in Figure 5.  
According to the Kmeans clustering method mentioned in Section 2.2.4, 179 control points are adaptively selected from the calibration data set. The selection results of control points set is shown in Figure 6.  
Figure 5: Calibration data set. (**a**) is the world coordinate of the calibration dataset. (**b**) is the image coordinate of the calibration dataset.  
Figure 6: The selection results of control points set.
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 3 Results and Analysis
### Durability Properties  
#### 3.2.1 Water Absorption and Porosity  
The reduction in the mechanical properties of C5 upon the addition of calcium oxide is explained by observing the results of water absorption and porosity tests, as illustrated in Figure 5a,b. Incorporating calcium oxide in C5 resulted in an increase in absorption and porosity of 54.9% and 26.2%, respectively. The high alkali content within the composite matrix had a substantial influence on accelerating the slag chemical bonds. At the same time, incorporating CaO increased the concentration of Ca\({}^{2+}\) in the matrix. Therefore, the excessive amount of silicon and calcium led to the rapid formation of the silicate gel, producing larger particles [22,39,40,41]. Hence, the overlapping among the formed, large particles created larger spaces within the AAS composite. The presence of these voids was reflected in the water absorption and porosity tests. However, the increased values of water absorption and porosity were still lower than those exhibited by the control mix. This was linked to the microbial effect of partially filling the existing pores and generating new spaces, reducing the negative effect of the added calcium oxide.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 4 Results
### Performance Evaluation of SATLLA-2B
Moreover, despite actual losses of the \(Att_{loss}\) and \(Tur_{loss}\) parameters being greater than expected, an FSO link was successfully established with the expected performance. We then tested the ability of a ground station to detect an array of LEDs as located on SATLLA-2. The engineering model of SATLLA-2B was used to see if it can be detected from 1-10 km in good conditions (see Figure 9. Thus, we conjecture that using a 500 mm telescope with an FOV of 0.8 degrees should be able to detect SATLLA-2B from a 1000 km (on clear nights) as it has a \(100\times\) larger lens diameter and 1% the FoV. We conclude that the link budget as presented above implies a valid FSO communication estimation. Thus, an FSO link from a pico-satellite to an optical ground station can be obtained in optimal (clear night sky) conditions. **Long-range tracking:** In these experiments we tested the capabilities of the ground station to track an LEO satellite. Naturally, we used the ISS as the tracking target. Figure 6 demonstrates the ability of a wide FOV camera to detect and track the ISS. Figure 5 demonstrates the actual ability of a calibrated robotic telescope to capture an LEO satellite with high accuracy.
## 2 Materials and Methods
### Overview of Traditional A* and DWA Algorithms
#### 2.1.2 Traditional DWA Algorithm  
The traditional DWA algorithm, proposed by [PERSON] et al. [21], obtains a feasible velocity combination within a certain time window by combining the limiting velocity and acceleration constraints of the robot's own physical limitations with the obstacle constraints of the object environment \((v,w)\), where \(v\) and \(w\) denote linear and angular velocities, respectively. The three types of constraints can be expressed as follows:  
\[V_{s}=\left\{(v,w)|v_{\min}\leq v\leq v_{\max},w_{\min}\leq w\leq w_{\max}\right\} \tag{2}\]  
\[V_{d}=\left\{(v,w)\big{|}v_{c}-\hat{v}\Delta t\leq v\leq v_{c}+\hat{v}\Delta t,w_{c}-\hat{w}\Delta t\leq w\leq w_{c}+\hat{w}\Delta t\right\} \tag{3}\]  
\[V_{a}=\left\{(v,w)|v_{a}\leq\sqrt{2\cdot dist(v,w)\cdot\hat{v}},w_{a}\leq \sqrt{2\cdot dist(v,w)\cdot\hat{w}}\right\} \tag{4}\]where \(V_{s}\) is the self-limiting velocity constraint; \(V_{d}\) is the self-limiting acceleration constraint; \(V_{a}\) is the obstacle constraint; \(v_{c}\) denotes the current linear velocity; \(\hat{v}\) denotes linear acceleration; \(w_{c}\) is the current angular velocity; \(\hat{w}\) is angular acceleration; \(dist(v,w)\) denotes the distance between the trajectory and the nearest obstacle.  
The intersection of the three types of constraints determines the feasible velocity range:  
\[V_{r}=V_{s}\cap V_{d}\cap V_{a} \tag{5}\]  
By sampling all feasible velocity combinations according to the specified accuracy \((v,w)\), the predicted trajectory is formed and evaluated, and the one with the highest score is considered the ideal trajectory. The evaluation function is defined as follows:  
\[E(v,w)=\sigma(\alpha\cdot head(v,w)+\beta\cdot dist(v,w)+\gamma\cdot vel(v,w)) \tag{6}\]  
where \(\alpha\), \(\beta\), \(\gamma\) are the weight coefficients; the \(head\) function refers to the difference between the heading angle at the end of the trajectory and the azimuth angle of the target; the \(dist\) function calculates the distance between the target and the nearest obstacle; the \(vel\) function is the linear velocity magnitude; \(\sigma\) denotes the normalization process.
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
###### Abstract  
Assessing the behavior of earth dams under dynamic loads is one of the most significant problems with the design of such large structures. The purpose of this study is to provide new models for predicting dam dispersion in real earthquake conditions. In the first phase, 103 real cases of deformation in earth dams were collected and analyzed due to earthquakes that occurred over recent years. Using nonlinear and machine learning techniques, i.e., group method of data handling (GMDH) and multivariate adaptive regression splines (MARS), two models for prediction of the slope deformation in earth dams under the various types of earthquakes were applied and developed. The main parameters used in these simulation techniques were earthquake magnitude (Mw), fundamental period ratio (Td/Tp), yield acceleration ratio (ay/amax) as inputs and value of slope deformation (Dave) as output. Finally, in order to check the accuracy of the results of the new models, a comparison was made with the previous relations and models in seismic conditions for the slope deformation in earth dams. The results showed that the MARS model, which is able to provide a mathematical equation, has a better result than the GMDH model. These new models are recommended to be used for future analyses based on their flexible capabilities.  
s 25 January 2020; 17 February 2020; Published: 21 February 2020