esa-sceva/satcom-chunk-collection huggingface dataset:
# Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains
## 3 PF-PMC-PHD Filter
Recalculating the weights of particles using measurements \(\mathbf{z}\in Z_{k}\) from the sensor, and the posterior probability density function \(v_{k}(\mathbf{x},\mathbf{y})\) of pair \((\mathbf{x},\mathbf{y})\) writes as  
\[v_{k}(\mathbf{x},\mathbf{y})=v_{k}^{1}(\mathbf{x},\mathbf{y})+v_{k}^{2}(\mathbf{x},\mathbf{y}) \tag{13}\]  
\[v_{k}^{1}(\mathbf{x},\mathbf{y})=\sum\limits_{i=1}^{L_{k-1}+I_{k}}\widetilde{w}_{k}^{1,(i)}\delta((\mathbf{x},\mathbf{y})-(\widetilde{\mathbf{x}}_{k}^{(i)},\widetilde{\mathbf{y}}_ {k}^{(i)})) \tag{14}\]  
\[\widetilde{w}_{k}^{1,(i)}=\Big{(}1-p_{D,k}(\widetilde{\mathbf{x}}_{k}^{(i)})\Big{)} \widetilde{w}_{k|k-1}^{(i)} \tag{15}\]  
\[v_{k}^{2}(\mathbf{x},\mathbf{y})=\sum\limits_{\mathbf{z}\in Z_{k}}\sum\limits_{i=1}^{L_{k-1 }+I_{k}}\widetilde{w}_{k}^{2,(i)}(\mathbf{z})\delta(\mathbf{x}-\widetilde{\mathbf{x}}_{k}^ {(i)})\delta_{\mathbf{z}}(\mathbf{y}) \tag{16}\]  
\[\widetilde{w}_{k}^{2,(i)}(\mathbf{z})=\frac{p_{D,k}(\widetilde{\mathbf{x}}_{k}^{(i)})q_ {k}^{(i)}(\mathbf{z})\cdot\widetilde{w}_{k|k-1}^{(i)}}{\mathbf{\kappa}_{k}(\mathbf{z})+ \sum\limits_{i=1}^{L_{k-1}+I_{k}}p_{D,k}(\widetilde{\mathbf{x}}_{k}^{(i)})q_{k}^{( i)}(\mathbf{z})\widetilde{w}_{k|k-1}^{(i)}} \tag{17}\]  
\[q_{k}^{(i)}(\mathbf{z})=\mathrm{N}(\mathbf{z};\widetilde{\mathbf{y}}_{k|k-1}^{(i)};\Sigma_ {k}^{22}) \tag{18}\]  
**Step 4:** Resampling of the particles. Estimating the number of targets:  
\[\hat{N}_{k}=\sum\limits_{i=1}^{L_{k-1}+I_{k}}\widetilde{w}_{k}^{1,(i)}+\sum \limits_{\mathbf{z}\in Z_{k}}\sum\limits_{i=1}^{L_{k-1}+I_{k}}\widetilde{w}_{k}^{2,( i)}(\mathbf{z}) \tag{19}\]Resampling particles \(\left\{\widetilde{w}_{k}^{1,(i)}/\hat{N}_{k},(\widetilde{x}_{k}^{1,(i)},\widetilde {y}_{k}^{1,(i)})\right\}_{i=1}^{L_{k-1}+l_{k}}\cup\left\{\left\{\widetilde{w}_{ k}^{2,(i)}(\boldsymbol{z}_{j})/\hat{N}_{k},(\widetilde{x}_{k}^{2,(i)}, \boldsymbol{z}_{j})\right\}_{i=1}^{L_{k-1}+l_{k}}\right\}_{j=1}^{|Z_{k}|}\), meanwhile, keeping the value \(\boldsymbol{y}\) of particles which represent \(v_{k}^{2}(\boldsymbol{x},\boldsymbol{y})\), we will obtain particles \(\left\{w_{k}^{(i)}/\hat{N}_{k},(\boldsymbol{x}_{k}^{(i)},\boldsymbol{y}_{k}^ {(i)})\right\}_{i=1}^{L_{k}}\). **Step 5:** Approximation of posterior probability density. Rewrites the posterior joint PHD \(v_{k}(\boldsymbol{x},\boldsymbol{y})\) of pair \((\boldsymbol{x},\boldsymbol{y})\) as  
\[v_{k}(\boldsymbol{x},\boldsymbol{y})=\sum_{i=1}^{L_{k}}w_{k}^{(i)}\delta(( \boldsymbol{x},\boldsymbol{y})-(\boldsymbol{x}_{k}^{(i)},\boldsymbol{y}_{k}^ {(i)})) \tag{20}\]  
According to \(v_{k}(\boldsymbol{x})=\int v_{k}(\boldsymbol{x},\boldsymbol{y})d\boldsymbol{y}\), and \(\int\delta(\boldsymbol{y}-\boldsymbol{y}_{k}^{(i)})d\boldsymbol{y}=1\), \(i=1,\cdots L_{k-1}+l_{k}\), the posterior PHD \(v_{k}(\boldsymbol{x})\) of state \(x\) writes as  
\[v_{k|k-1}(\mathbf{x},\mathbf{y})=\sum\limits_{i=1}^{L_{k-1}+I_{k}}\widetilde{w}_{k|k-1 }^{(i)}\cdot\delta((\mathbf{x},\mathbf{y})-(\widetilde{\mathbf{x}}_{k}^{(i)},\widetilde{ \mathbf{y}}_{k}^{(i)})) \tag{12}\]0
# Molecular Dynamics-Based Study of Graphene/Asphal Mechanism of Interaction
## 3 Results and Discussion
### Interaction between Graphene and Asphalt Components
#### 3.2.3 Glass Transition Temperature  
A lower Tg generally indicates a better low-temperature performance of the asphalt material because it is still flexible at lower temperatures, thereby reducing the likelihood of cracking. In this study, 100 ps kinetic calculations were carried out at 200 K, 220 K, 240 K, 260 K, 280 K, 300 K, 320 K, and 340 K temperatures under the NPT system (one standard atmospheric pressure) for the asphalt molecular model that reached the steady state, and the 80 th ps to 100 ps density data were extracted for each test temperature and statistically analyzed. The reciprocal of the average density value at this stage (specific volume) was used as the dependent variable to plot the change in a particular volume of graphene-modified asphalt at different test temperatures.  
Figure 13 shows the specific volume of 70# and PGR (G7-1) modified 70# matrix asphalt at different temperatures. This study defines the intersection of the two specific volume-fitted straight lines in the asphalt material as the glass transition temperature.  
The glass transition temperatures Tg of different types of graphene-modified asphalt are shown in Figure 14. The increase in the size and number of layers of PGR leads to an increase in the Tg of the 70# matrix asphalt. Still, the fluctuation range of the Tg is not more than 5 K when the size of the PGR does not exceed G9-1, which indicates that the addition of PGR has a slight effect on the low-temperature performance of asphalt but is not sufficient to change the low-temperature performance class.  
The Tg of SBS-modified asphalt decreased after adding Small PGR (e.g., G5-2), and the increase in the number of PGR layers increased the Tg, thus reducing its low-temperature cracking resistance. However, this effect is negligible, considering that the PGR incorporation in the test was not more than 1%, which is much lower than the percentage used in the simulation.  
For ACR-modified asphalt, although its Tg is much higher than that of 70# and SBS asphalt, which suggests that the low-temperature performance of ACR is not as good as that of the latter two, the opposite is true. It should be noted that Tg is not a fixed value but a range. This study focuses on the law of influence of different types of PGR on the Tg of asphalt matrix. Figure 14 shows that small-sized graphene slightly reduces the Tg of ACR-modified bitumen, while the opposite is true for increasing the number of layers.  
Figure 13: Specific volume change of asphalt molecular system at different temperatures.  
Overall, adding graphene can appropriately enhance the low-temperature performance of ACR-modified asphalt without exceeding the size of G9-1.
## 4 Conclusions  
A 30 cm soil column study was performed with and without vertical holes (occupying 0.8% of the soil volume and 3% of total surface area), and corresponding TPH removal efficiencies were evaluated at three depths (5, 15, and 25 cm). As compared to the column without holes, the vertical holes improved the water content in the soil columns suggesting better transport and distribution of remedial agents throughout the soil. As such, an enhanced TPH degradation result was obtained from lower depths in the soil column having vertical holes, achieving a TPH degradation efficiency of 88-90%. In contrast, only 71-72% of TPH was removed from the lowest depth in the column without vertical holes.  
An exponential increase in bacterial population (CFU) with a similar number (\(10^{7}\) CFU/g soil) in all soil depths was observed after treatments through holes. The results  
Figure 6: Change in soil microorganisms in three different soil depths (5, 15, and 25 cm from the surface) during persulfate oxidation and bioremediation of TPH.  
demonstrated that application through vertical hole oxidation followed by bioaugmentation is an effective method to increase the following: evenly distributed reactive reagents throughout the soil, maintenance of soil pH condition, and exponential bacterial growth. The results show the possibility of a simple natural delivery method of remediation agents to the unsaturated contaminated soil.  
The following are available online at [[https://www.mdpi.com/2076-3](https://www.mdpi.com/2076-3)]([https://www.mdpi.com/2076-3](https://www.mdpi.com/2076-3)) 417/11/2/781/s1, Figure S1: Foam sprayed soil columns. Figure S2: Change in volumetric water content (VWC) in soil columns using vertical holes during (a) persulfate oxidation (PS) for two days, and (b) oxidation followed by bioaugmentation (PS + bio) for 4 weeks. Note that the VWC was continuously monitored during experimental period (one month). Figure S3: Volume of leachate collected from the bottom of soil column as a function of time. Figure S4: Simple laboratory set-up to identify whether foam was propagated through the holes or remedial agent after the foam broken. Figure S5: Surfactant foam bubbles at different nodal angles and plateau border lengths for (a) pure AOS, (b) pre-oxidation, and (c) bio-augmentation. Table S1: Physicochemical properties of experimental soil. Table S2: Properties of the different surfactant foams applied in this study. Table S3: Change in microbial population (CFU/g) of soil during remediation. Table S4: Change in pH of soil during remediation. Text S1: Foam characterization.  
Conceptualization, R.B. and S.-W.J.; methodology, R.B. and P.G.; validation, R.B., P.G., and S.-W.J.; writing--original draft preparation, R.B. and P.G.; writing--review and editing, S.-W.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (2018R1A2B6006139).  
Not applicable.  
The authors declare no conflict of interests.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 3 Experiment Results
### Ablation Study
Comparing the pre-disaster and post-disaster buildings in Figure 10a,b, due to the tsunami, it can be recognized that a giant deviation of the buildings emerged after the disaster, but the structures of the buildings are still largely preserved. However, such buildings are taken as undamaged in the ground truth masks in xBD. The model with SF notices the structural connections of the deviant houses and shows a better performance in this position. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Methods** & **Score (\%)** & \(F1_{loc}\) (\%) & \(F1_{cls}\) (\%) & \(F1_{C_{1}}\) (\%) & \(F1_{C_{2}}\) (\%) & \(F1_{C_{3}}\) (\%) & \(F1_{C_{4}}\) (\%) \\ \hline Baseline (Res-50) & 78.5 & 85.9 & 75.4 & 92.2 & 57.0 & 76.3 & 86.3 \\ SDAFormer (without SF) & 79.5 & 86.1 & 76.7 & **92.6** & 59.6 & 76.9 & 86.5 \\ SDAFormer (with SF) & **80.2** & **86.1** & **77.6** & 92.5 & **61.4** & **77.5** & **86.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study of the proposed method (\(F1_{C_{1}}\): \(F1\) score of no damage; \(F1_{C_{2}}\): \(F1\) score of minor damage; \(F1_{C_{3}}\): \(F1\) score of major damage; \(F1_{C_{4}}\): \(F1\) score of destroyed). Figure 8: The visual comparison of prediction results on hurricane–Harveyan xBD dataset. (**a**) Pre-disaster; (**b**) post-disaster; (**c**) ground truth; (**d**) baseline; (**e**) SDAFormer (without SF); (**f**) SDAFormer (with SF). Figure 9: The visual comparison of prediction results on hurricane-Michael in xBD dataset. (**a**) Pre-disaster; (**b**) post-disaster; (**c**) ground truth; (**d**) baseline; (**e**) SDAFormer (without SF); (**f**) SDAFormer (with SF).
## 3 Results  
### Diurnal Dynamics of SIF and GPP at the Canopy Scale  
We analyzed the dynamic variations in GPP, SIF, and PAR for the maize canopy on individual clear-sky days at the diurnal timescale over the whole growing season (Figure 3). The SIF and GPP showed a strong diurnal pattern with a steady increase during the morning and subsequent decrease after solar noon, which was mainly driven by the incoming radiation. Both the SIF and GPP exhibited gradual increases with PAR at the diurnal timescale (Figure 4). The diurnal relationships between GPP and PAR exhibited a clear asymptotic trend, while the SIF-PAR relationships showed a more linear trend. In addition, the structural and physiological variables had a significant effect on the saturation of GPP over the whole growing season; however, this effect was not considered in this study. Overall, the relationships between the canopy SIF\({}_{760}\) and GPP were asymptotic at the diurnal scale, and PAR was the key factor driving the relationship between the SIF and GPP.  
Figure 3: The diurnal variations in canopy gross primary production (GPP, black), far-red solar- induced fluorescence (SIF\({}_{760}\), dark red), and photosynthetically active radiation (PAR, purple) under clear-sky conditions at the DM flux site in 2017. DOY represents the day of the year. The first row represents the vegetative stage (DOY 162, DOY 174, and DOY 187) and the second row indicates the reproductive stage (DOY 192, DOY 224, and DOY 264).
## 3 Discussion of Experimental Results
### Discussion of Experimental Results
#### 3.3.3 Optimization of Injection Timing  
In the production process, when monitoring the transportation of oil during the pressure drop process, when the pressure decreases, and when oil transportation no longer occurs, this is considered to be the best time to huff and puff. From the production results after each round of huff and puff, it can be seen that in the first two rounds of huff and puff, when the pressure drops to about 74% of the formation pressure, the oil recovery is not evident. It is recommended to inject CO\({}_{2}\) when the pressure drops to about 70%; if the huff and puff is continued after two rounds of huff and puff, the timing of injection needs  
Figure 14: NMR curve changes throughout the process.  
Figure 15: Degree of extraction of different rounds of huff and puff rounds (The blue line shows the extent of exploitation in different rounds).  
to be advanced, and it is recommended that CO2 be injected for the subsequent huff and puff before the formation pressure drops to 80% (Figures 4f, 6f, 8f and 10b).
# SINDVis: User-Centered Dynamic Interactive Visualization System for Space Information Networks
## 2 Related Work
### SIN Concept and Architecture
In accordance with the original intention of the construction and development of SIN, it is mainly based on the space platforms to achieve information acquisition and processing as well as the task of information assurance. Therefore, the main tasks of the ground terminals are to maintain and manage space entities. We believe that the Ground layer of SIN is mainly composed of the following types of terminals: fixed-based stations, hand-held mobile-based stations, vehicle-based stations, and carrier-based stations. The composition structure of the Ground layer terminals is shown in Figure 4._  
Based on **Definitions 1-5**, we can design the top-level of SIN architecture. The basic design ideas are building space-based backbone networks, including GEO and MEO/LEO layer satellite systems to solve global coverage issues, building a near-space backbone network including the Near Space layer information system to solve regional strengthening issues, and building Ground layer terminals to achieve command and control. Therefore, the SIN has a more obvious four-layer structure.
## 2 Scientific Instrument FXE for Chemical Dynamics Studies  
### From the Undulator Exit to the Experimental Hall  
European XFEL is a user research laboratory located in the western part of the Hamburg metropolitan area, reaching from the DESY campus in Hamburg Bahrenfeld to the town of Schenefeld in Schleswig-Holstein. The initially three undulator sources provide intense flashes of tunable X-radiation from the carbon K edge to very hard X-rays, or 250 eV to 25 keV in the fundamental lasing photon energy [32]. The X-radiation is delivered in bursts (or trains) of up to 2700 pulses every 100 ms (and each pulse in this burst spaced by 220 ns at 4.5 MHz), and can thus deliver up to 27,000 pulses every second. While the machine performance is still being ramped up, 2 m) single pulse energy were already achieved at 9.3 keV photon energy, operating presently at up to 8 W of radiation power, corresponding to an effective repetition rate of 4000 pulses/s.  
The scientific instrument FXE (Femtosecond X-Ray Experiments) is located at the end of the SASE1 FEL undulator source (Figure 1). Two offset mirrors M1 and M2 allow rejection of high energy synchrotron radiation, and a third deflection mirror M3 guides the X-ray beam on a side-branch path into the FXE experimental area [32], while the straight path (with retracted M3) delivers X-rays into the neighboring scientific instrument SPB/SFX for single particle bio-imaging and serial femtosecond crystallography experiments [33].  
The XFEL radiation for FXE is collimated by a collection of compound refractive beryllium lenses (CRLs in Figure 1) to a beam size of around 1 mm diameter (for all energies in the 5-20 keV range) 230 m from the undulator exit or 670 m from the experimental hutch. Shortly before the beam enters the experimental hutch it passes through a (retractable) 4-bounce Si(111) primary monochromator (\(\Delta\)E/E = 10\({}^{-5}\)-10\({}^{-4}\)), which preserves the same beam path for both pink and monochromatic beams. This property thus allows us maintain the same X-ray spot on the sample in either operation mode, and thus requires no further spatial overlap search when switching from pink to monochromatic beams. While the X-ray pulse arrival time will change between both modes, this arrival time change can be calculated and thus corrected for with the laser beam (more details below).
# A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
## 2 Theoretical Background and Previous Studies
### Machine Learning Methodology
#### 2.3.3 K-Nearest Neighbor  
K-nearest neighbor (KNN), proposed by [PERSON] and [PERSON], is one of the most widely used predictive models. The prediction process is as follows: (1) Calculating the distance between the target and other known observations; (2) Extracting \(K\) neighbors that are close to the target in terms of the calculated distance; (3) Obtaining the value of the \(K\) neighbors; (4) Calculating the predicted value of the target. The regressive and classified results of the target are the average and mode value of the \(K\) neighbors, respectively [39]. Thus, the prediction of the target is affected by the number of neighbors (\(K\)) around the target and the selection of the distance function. Predicted results of the target based on a few neighbors are not sufficiently convincing, whereas excessive neighbors may contain noise that can lead to erroneous predictions. That is, a suitable \(K\) value is essential for the prediction performance of KNN. In addition, the Euclidean distance function is defined as the physical distance between two observations [40], expressed as follows:  
\[d_{ij}=sprt\Big{(}\big{(}x_{i}-x_{j}\big{)}^{2}+\big{(}y_{i}-y_{j}\big{)}^{2} \Big{)} \tag{5}\]  
where \(d_{ij}\) represents the Euclidean distance between observations \(i\) and \(j\). Observation \(i\) is written as \(\{x_{i}=[x_{i1},x_{i2},x_{i3},\cdots,x_{iM}],y_{i}\}\); observation \(j\) is represented as \(\{x_{j}=[x_{j1},x_{j2},x_{j3},\cdots,x_{jM}],y_{j}\}\).
# Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
## 5 Results
### Docking of an AUV onto a Fixed Platform  
The learning behaviour of the DQN and DDPG algorithms is shown in Figure 8, which shows the convergence with episode number of the reward per episode averaged over a moving window of 10 episodes. The research was averaged over five training sessions per algorithm, initialised with different seed numbers. The shaded region indicates the values within the minimum and maximum observed average reward values.  
The performance of the AUV during the docking manoeuvre under the different control strategies is shown in Figures 9-11. Figure 9 shows the time evolution of the translations in surge and heave and the rotation in pitch. The docking point was placed at \(x=20\) m and \(z=0\) m and zero pitch angle was desired. The time evolution of the translational and rotational velocities can be seen in Figure 10. The control input to the motor and sterplane is shown in Figure 11 for the different control schemes in addition to the motor rotational speed.  
Figure 8: Convergence of the average reward per episode with episode number for the DQN and DDPG algorithms.  
Figure 7: Diagram of the neural networks used: to approximate the action-value function by the critic (**a**); and to select the action that maximises the discounted reward by the actor (**b**) of the DDPG algorithm.  
Figure 10: Translational and rotational velocity of the AUV during docking with different control strategies.  
Figure 9: Translation and rotation of the AUV during docking with different control strategies.  
The DQN and DDPG curves show results after learning for 10,000 episodes. During learning, the starting vertical position of the AUV was randomly initialised in the range [0.9,1.1], as recommended by [PERSON] et al. [32]. However, here, the starting position was fixed at \(z=1\) m.  
Note that the signals had a different duration for each control algorithm, since the docking criteria (as defined by Equation (17)) were met at different times.  
Table 5 summarises the computational time required by each control scheme during deployment and training when running on an Intel Core i7-6700 quadcore processor with 32 GB RAM with the selected MATLAB/Simulink implementation. In fact, only the order of magnitude is shown to enable a clearer comparison. Note that training is required only for the RL algorithms.
## 2 Materials and Methods
### Determination of the Antioxidant Activity  
#### 2.4.1 DPPH Free Radical-Scavenging Activity Assay  
The DPPH radical scavenging assay was done, following the method described by [22], with some modifications. Indeed, increasing concentrations of the aqueous ZLF extract (6, 12, 25, 50, 100, 200, and 400 \(\upmu\)g/mL) were prepared in the test tubes, then taken 0.1 mL from each tube and mixed with 2.5 mL of the ethanolic solution of the DPPH (4%). The mixtures were then incubated in the dark at room temperature for 30 min. After incubation, the absorbance of the mixtures was immediately measured by spectrophotometry at a wavelength of 517 nm. Ascorbic acid was used as a reference (a standard antioxidant). All tests were done in triplicate. The trapping activity of our extract was calculated using the following formula:  
\[\text{DPPH trapping effect }(\%)=\frac{A_{0}-A_{1}}{A_{0}}\times 100\]  
\(A_{0}\) is the absorbance of the control reaction, and \(A_{1}\) is the absorbance of all of the extract samples and standards.