esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Dataset
### ALOS and ALOS-2 Data  
The first SAR dataset was acquired by the Japanese Advanced Land Observing Satellite (L-band Synthetic Aperture Radar ALOS PALSAR). The wavelength of ALOS PALSAR is 23.62 cm with a pixel resolution of 12.5 m and a swath of 70 km. The incidence angles range between 36\({}^{\circ}\) and 38\({}^{\circ}\). The images correspond to the ascending orbit that is northeast-looking [55]. In this study, a total of 66 scenes of ascending stripmap mode ALOS PALSAR data along four adjacent tracks covering seven frames from 2007 to 2009, were collected in the northern QTP. The acquired ALOS data are shown in Table 1. The second SAR dataset was acquired by the Advanced Land Observing Satellite-2 (L-band ALOS-2 PALSAR-2)  
Figure 1: The study area with topography and surface traces of four overlapping ALOS ascending tracks, three ALOS-2 ascending tracks and two Sentinel-1 descending tracks in the northern QTP.  
launched in May 2014 by the Japan Aerospace Exploration Agency (JAXA). The revisit cycle of the ALOS-2 PALSAR-2 is 14 days [56]. In this study, a total of 73 scenes of ascending ALOS-2 PALSAR-2 data along three tracks covering four frames from 2015 to 2020 were collected in the northern QTP. The acquired ALOS-2 data are shown in Table 2.
# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 4 Experiment Results and Analysis
### Qualitative Analysis  
As depicted in Figures 7 and 8, the images recovered using the proposed MARCAFNet were compared side-by-side with those derived using other state-of-the-art methods. The blue and red bounding boxes are presented as enlargements in the region of the image. The zoomed-in area is used to clearly present the difference between the proposed method which outperformed other state-of-the-art methods. Figures 7 and 8, respectively, present the results obtained using Rain100H and Rain100L. Figure 9 presents the results obtained using a real-world dataset shared by [14]. In these tests, the other state-of-the-art methods exhibited some weaknesses. The edge details of the trees are blurred in the red boxes in the first row of Figure 7b,e,h,i. In Figure 7, the rain streaks have not been removed and are visible in the red boxes in the first row of (c), (d), (f), and (g). The details of the girl's hair have been blurred, as is visible in the blue and red boxes in the second row of Figure 7b-i. The proposed MARCAFNet improved detail blurring and removed rain streaks, as indicated by Figure 7j. The bucket hat details are blurry and not clearly visible in the red and blue boxes in the first row of Figure 8b-i. In Figure 8, the rain streaks have not been removed, as shown in the red and blue boxes in the second row of (c), (e), (f), (h), and (i). Detail blurring and rain streak removal were superior when the proposed MARCAFNet was employed, as depicted in Figure 8j. In short, the blurring and smoothing of image details resulted from an inability to distinguish between rain streaks and texture features in the images.  
The source of the deraining result image depicted in Figure 9 is the real-world image shared by [15]. In Figure 9, the rain streak has not been removed on the clothes and is still visible in the blue boxes in the first row for (b)-(h). The folds of the pants have been blurred in the red boxes in the first row for (b)-(h). The intervals of the tiles are blurred, as visible in the red and blue boxes in the two rows of (b)-(h). Detail blurring and rain streak removal were improved when the proposed MARCAFNet was used, as illustrated in Figure 9i.  
Figure 7: Deraining task performance of the proposed MARCAFNet and state-of-the-art methods when using the Rain100H dataset. (**a**) Rainy image, (**b**) RESCAN, (**c**) PReNet, (**d**) LPNet, (**e**) MSPFN, (**f**) MPRNet, (**g**) VRGNet, (**h**) PCNet, (**i**) RAiANet, and (**j**) MARCAFNet, and (**k**) ground truth.
## 6 Repeatability Tests  
SMTCD was measured with a ST and compared with the standard value (after correction). As shown in Figure 12, the average value and standard deviation (indicating repeatability) were calculated after 10 repeated measurements. Finally, the indication error of SMTCD was fitted to a linear function of L.  
The repeatability test results are shown in Table 1. The maximum standard deviation of 0.014 mm (14 \(\upmu\)m) indicates the repeatability of the SMTCD, which is better than the performance index of the device (repeatability: 20 \(\upmu\)m). By linear fitting, the fitting equation of value error is y = -3E-06L-0.0018; the indication error of the SMTCD does not exceed the maximum permissible error: \(\pm\)(0.03 + 3 \(\times\) 10\({}^{-5}\) L) mm (L unit: m). So, the SMTCD can carry out the verification and calibration of SMT and has a good value of popularization and application.  
Figure 11: Highest scribed line recognition.  
Figure 12: Repeatability test of SMTCD.  
The variation trend of the indication error value of each test object is consistent, and the indication error increases with the increase in the verification points.  
According to the regulations' indication error requirement [19], the limits of the indication error are different for each point. The average value of 10 test results is taken as the indication error of each point and compared with the upper and lower allowable error limits. The indication error of each point is within the allowable error limits, i.e., the verification of the indication error is completed.
## 2 Results and Discussion  
### Copolymerization of _rac_-LA and CL  
Copolymers of _rac_-LA and CL were synthesized during the ROP process in two 1-butyl-3-methylimidazolium cation-based ILs with two different anions: tetrafluoroborate and bis(trifluoromethylsulfonyl)imide at 80 and 110 \({}^{\circ}\)C for 7 to 14 days at various molar ratios of _rac_-LA/CL. The reactions were carried out using 100 mg (10 wt. %) _Candida antarctica_ lipase B (CALB) as catalyst. Water from impurities (free water from an enzyme and ionic liquids) acts as a co-initiator [22]. Thermogravimetric analysis revealed that there was about c.a. 1.0 wt. % of free water in CALB [12] and c.a. 0.1 wt. % [12] and c.a. 1.3 wt. % in [bmim][NT\({}_{2}\)] and [bmim][BF\({}_{4}\)], respectively (Figure S1, Supplementary Materials). The effects of polymerization parameters on the chain microstructures of the synthesized polyesters were investigated.  
The structure of the synthesized copolymers was analyzed by \({}^{1}\)H-NMR and \({}^{13}\)C-NMR. The \({}^{1}\)H-NMR spectrum (CDCl\({}_{3}\), 300 MHz) presented in Figure 1 confirmed the structure of the copolymers of _rac_-LA and CL.  
The signals centered at 5.15 and 1.54 ppm were assigned to the lactide methine protons (CH, H\({}_{\rm E}\)) and lactide methyl protons (CH\({}_{3}\), H\({}_{\rm F}\)). The signal at 4.35 ppm was assigned to the protons next to the end-functional hydroxyl groups of lactide (CH(CH\({}_{3}\))OH, H\({}_{\rm X}\)). Protons from heterodyads cause the signals at 4.16 ppm (CH\({}_{2}\)O, H\({}_{\rm D1}\)) and at 2.39 ppm for (CH\({}_{2}\), H\({}_{\rm A1}\)). The signals at 4.07 (CH\({}_{2}\)O, H\({}_{\rm D}\)) and 2.31 ppm (CH\({}_{2}\)CO, H\({}_{\rm A}\)) correspond to the \(\varepsilon\)- and \(\alpha\)-methylene protons of the PCL. Another feature is the chemical shift for protons from methylene groups of PCL next to the end-functional hydroxyl groups, which appear as a triplet at 3.66 ppm (CH\({}_{2}\)OH, H\({}_{\rm Y}\)). Two other multiplets at c.a. 1.65 ppm and 1.40 ppm were attributed to the protons of the methylene groups of the PCL chain (CH\({}_{2}\), H\({}_{\rm B}\), H\({}_{\rm C}\)).
## 2 Method
### Mesh Sensitivity Analysis  
Four meshing cases were studied for the mesh sensitivity analysis, in which the base mesh sizes gradually became finer, as listed in Table 2. Table 2 also shows the mesh numbers and the calculation times for all the cases.  
The monitoring points were set in the areas where the wind speeds changed greatly. In total, 66 monitoring points were in the vertical plane at y = 0, and 82 monitoring points were in two horizontal planes at z/b = 0.125 and z/b = 1.25. These monitoring points were distributed around the target building, as shown in Figure 5.  
To compare the difference among the above four meshing cases, the error rate proposed by [PERSON] et al. (2018) was defined as shown below.  
\[\text{Error Rate}=\frac{\sum\sqrt{\left(\frac{v_{0}-v_{x}}{v_{0}}\right)^{2}} }{n}\]  
where \(v_{0}\) is the value of the finest mesh (Case 4 in Table 2; base mesh size of 0.02 m), \(v_{x}\) is the value of the other mesh (other cases in Table 2; base sizes of 0.1 m, 0.06 m, and 0.04 m), and n is the number of monitoring points.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Base Mesh Size** & **Architectural** & **Mesh Numbers** & **Calculation** \\  & **(m)** & **Mesh Size (m)** & **Surface** & **Time (s)** \\ \hline Case 1 & 0.10 & 0.01 & 977 & 152 \\ \hline Case 2 & 0.06 & 0.006 & 2869 & 865 \\ \hline Case 3 & 0.04 & 0.004 & 5944 & 6235 \\ \hline Case 4 & 0.02 & 0.002 & 23,386 & 81,154 \\ \hline \end{tabular} Legend: Building mesh size refers to the mesh size of the building surface. The ratio of the building mesh size to the base mesh size is 10%.  
\end{table}
Table 2: Mesh and calculation times of 4 cases.  
Figure 4: Location and size of blocks.
# Lightweight Path Recovery in IPv6 Internet-of-Things Systems
## 6 Conclusions  
In this paper, we propose a lightweight path recovery algorithm in a dynamic network, namely, LiPa. Based on the path correlation inference in the network topology, LiPa reconstructs the routing path of each packet within a sliding window that varies according to the activity the ratio of nodes. The distinctive feature of LiPa is that the path recovery is performed in two dimensions: temporal information and spatial information. We analyze the performance improvement effect of both dimensions of LiPa and compare it with two related approaches. The experimental results show that LiPa exhibits superior performance in different network settings.  
For the limitations, the system model denotes the target scenario of our work is a wireless sensor network (WSN) with fixed-location nodes. The proposed LiPa cannot be appropriately applied in mobile networks (e.g., vehicular networks, UAV networks) whose path correlations are weak and frequently change due to the movement of nodes.  
In future work, an asymmetric network with RPL routing should be further exploited. The uplink and downlink routing is assumed to be symmetric in our present work. However, when this assumption is not satisfied, both the uplink and downlink paths should be reconstructed for point-to-point path recovery.  
Investigation, L.F.; Methodology, Z.L.; Supervision, Z.Z.; Validation, M.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key Research and Development Program of China under Grant No.2020 YFE0200500.  
The authors declare no conflict of interest.
## 3 Multimetric Map-Aware Routing Protocol (MMMR)
### Map-Aware Capability and Use of a Local Buffer
We use the LOS (line of sight) criteria, because our evaluation scenario is a city, _i.e._, an urban zone with walls and buildings. Besides, the evaluated network is a VANET (nodes are vehicles). Thus, in this case, the more important and restrictive point to take into account is the line of sight of the nodes in the network. Due to that, in the moment the forwarding decision is made, we act in a conservative way, considering only as candidates to be the next forwarding node those nodes that actually can receive the packet, because they are in the LOS of the current carrying node. Concluding, three conditions must be verified for a node to be chosen as the next forwarding node, _i.e._, being in the coverage range, in LOS (line of sight) and nearer to a destination than the current node. If the candidate fulfills all three requirements, packets are forwarded to it; otherwise, packets are stored in the local buffer of the current carrier node. In addition to these two improvements, the decision of the next forwarding node is based on the combination of four metrics, which are detailed in Section 3.4.
###### Abstract  
We report on a mode-locked erbium-doped fiber laser delivering highly-chirped pulses with several tens of nanojoules of energy around 1560 nm and its exploitation to efficiently pump a fiber optical parametric oscillator (FOPO), thus enabling picosecond pulse generation around 1700 nm. The laser cavity features a high normal dispersion and mode-locking is sustained using tailored spectral filtering combined with nonlinear polarization evolution and a semiconductor saturable absorber. Numerical simulations show that the laser dynamics is governed by a strong mode-locking mechanism compensating for the large spectral and temporal pulse evolution along the cavity. In the frame of high energy picosecond pulse generation around 1700 nm, we then demonstrate that using highly-chirped pulses as pump pulses allows for the efficient tuning of the FOPO idler wavelength between 1620 and 1870 nm. In addition, satisfying noise characteristics have been achieved both for the Er-laser and the FOPO, with respective relative intensity noises (RIN) of \(-154\) and \(-140\) dBc/Hz, thus paving the way for the use of such sources in ultrafast instrumentation.  
erbium fiber laser; mode-locked lasers; parametric oscillators; dissipative solitons +
Footnote †: journal: Applied Sciences
## 3 Results
### The Joint Effects of Air Pollution Exposures on the Risk of Low Birth Weight  
Table 5 presents the joint effects of CO and O\({}_{3}\) and of PM\({}_{2.5}\) and O\({}_{3}\) on the risk of LBW\({}_{3000}\). The assessment of potential effects related to simultaneous CO and O\({}_{3}\) exposures showed that there was a synergistic effect on the risk of LBW\({}_{3000}\): the joint effect of \"high CO and high O\({}_{3}\)\" exposure on LBW\({}_{3000}\) was related to an adjusted RR of 1.88 (95% CI: 1.20-2.95). Based on the estimate of RERI, there was a 108% (95% CI: 27%-494%) excess risk associated with the joint effect of CO and O\({}_{3}\) at their highest exposure levels when compared to the risk that would be expected based on summing their  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Low Birth Weight +3000 g**} & \multicolumn{2}{c}{**Spring-Summer**} & \multicolumn{2}{c}{**Autumn–Winter**} \\ \cline{2-7}
**Single and** & **Crude RR** & **Adjusted RR** & **Crude RR** & **Adjusted RR** & **Crude RR** & **Adjusted RR** \\
**Multipollutant Models** & **(95\% CI)** & **(95\% CI)** & **(95\% CI)** & **(95\% CI)** & **(95\% CI)** & **(95\% CI)** & **(95\% CI)** \\ \hline \(PM_{2.5}\)/(Q\({}_{4}\geq 22.1\)_right_)\({}^{3}  
independent effects. There was suggestive evidence of a synergistic effect between high PM\({}_{2.5}\) and high O\({}_{3}\) (RERI 0.72, 95% CI: \(-\)0.07-3.60), although the 95% CI included the null value 0. Table 4 shows that the similar pattern of joint effects was present also on LBW\({}_{2500}\), although the estimates of RERI were greater for both effects of CO and O\({}_{3}\) (4.03, 95% CI: 1.61-9.28) and PM\({}_{2.5}\) and O\({}_{3}\) (3.53, 95% CI: 1.19-8.70). Table 6 shows joint effects of the pollutants on LBW\({}_{3000}\) during spring-summer season. The joint effect of high PM\({}_{2.5}\) and high O\({}_{3}\) was substantially higher during the spring-summer season, with an adjusted RR of 4.30 (95% CI: 0.66-10.16), compared to what would be expected based on their independent effects (1.1 for PM\({}_{2.5}\) and 1.8 for O\({}_{3}\)). The relative risk due to interaction (RERI) was 2.80 (95% CI: 1.36-19.88). These observations should be interpreted with caution; although an increased adjusted RR was observed, the RR was not statistically significant, apart from RERI. However, the risk based on RERI was statistically significant, which suggests that PM\({}_{2.5}\) and O\({}_{3}\) enhance each other's effects. Interestingly, ozone--either alone or in combination with the other pollutants--increased the risk of LBW\({}_{3000}\) only when the pregnancy took place close to or during the cold season.
# An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
## 4 Simulation and Result Analysis
Herein, the block sizes are set to \(L=4\) and \(L=16\). All the step sizes of the NLMS, PNLMS, IPNLMS, BS-PNLMS, and BS-IPNLMS algorithms are the same. The values are 0.05, 0.1, and 0.2 for WGN, colored noise, and the speech signal, respectively. The small positive constants of the NLMS, PNLMS, BS-PNLMS, and BS-SMPNLMS algorithms are \(\varepsilon=\varepsilon_{\rm BS}=0.01\), the parameters of the PNLMS, BS-PNLMS, and BS-SMPNLMS algorithms are \(\rho=5/N\), \(\delta=0.01\), and the bound of the BS-SMPNLMS algorithm is \(\gamma=0.01\). The adjusted parameter of the IPNLMS, BS-IPNLMS, and BS-SMIPNLMS is \(c=0\). The results of the comparison with different input signals are shown in Figures 8-10. Figure 8: Performance comparison of relevant algorithms for WGN. Figure 7: Influence of block size on the BS-SMIPNLMS for the speech signal. It is observed that the devised BS-SMPNLMS and BS-SMIPNLMS algorithms behave better than the related algorithms. For the WGN input signal, the devised algorithms have a faster convergence rate. For the colored noise and the speech signal, the devised algorithms have both faster convergence and smaller misalignment. Experiment 4. The parameter \(\gamma\) of the BS-SMPNLMS and BS-SMIPNLMS is a key parameter which can affect the convergence rate and the normalized misalignment. In this experiment, we study the performances of the BS-SMPNLMS and BS-SMIPNLMS algorithms, with \(\gamma=0.5,0.1,0.05,0.01,0.001\). The effects of \(\gamma\) on the BS-SMPNLMS and BS-SMIPNLMS algorithms are presented in Figures 11 and 12, respectively. Herein, we assume the input is the speech signal, and the \(SNR=30 dB\). Other parameters are set as follows: block size \(L=4\), \(\rho=5/N\), \(\delta=0.01\), \(\varepsilon_{\rm BS}=0.01\), \(c=0\). Figure 10: Performance comparison of relevant algorithms for the speech signal. Figure 9: Performance comparison of relevant algorithms for colored noise. We find that when \(\gamma\) decreases from 0.5 to 0.01, the normalized misalignment is reduced. However, the normalized misalignment stops decreasing when \(\gamma=0.01\) because the performances of \(\gamma=0.01\) and \(\gamma=0.001\) are almost the same. In previous experiments, we choose 0.01 as the value of \(\gamma\).
## 2 Background and Prior Art
### Hyperparameter Tuning  
'Hyperparameters' are parameters that are set before training begins. Examples of these parameters include learning rates, batch size, number of training epochs, optimizer, methods for weight initialization, etc.  
Learning rates control the magnitude of updates to weights during training. When learning rates are too large, training can diverge. If weights are too small, it may take a very long time to converge to a solution.  
An epoch is a cycle of training where all available data has been used once to train the network. A data set is often broken into batches and trained one batch at a time. The batch size can influence how well the architecture trains. The number of epochs also influences the overall performance of the network. If the network trains for too many epochs it can over train and will not generalize well. If trained too little, then the network will not learn the unique characteristics of the information presented to it. Either case can result in poor performance.  
Adam is currently among the most popular optimizers [18]. Ref. [27] found that Adam did not always perform better than other optimizers. In light of this, several additional optimizers were selected to include in the parameter search. A comparison of various optimizers was done by [PERSON] [28]. From their list, the following optimizers were selected for comparison: Adam, AdaMax [18], Nadam [29], and RMSProp [30].  
To perform a fair comparison between the selected CNN architectures, a hyperparameter search can be used to make sure architectures have been suitably configured. Selecting a good learning rate is considered to be one of the most important hyperparameters to tune [31]. For this research, optimizers and learning rates are evaluated.  
Approaches to hyperparameter tuning are an ongoing area of research. The most basic approach is a grid search, where a range of values is selected for each hyperparameter, the values are changed one at a time, and all combinations are exhaustively evaluated. This approach is computationally expensive. A more effective approach is a random search [32]. It is able to find better configurations in less time compared to grid search.  
Hyperopt is a parameter tuning approach that allows searching across multiple hyperparameters in an efficient way [33]. Ref. [33] shows that Hyperopt provides an order of magnitude speed-up over Bayesian parameter tuning methods. For this research, the Hyperopt algorithm was used from the Tune package for the learning rate search [34].
## 6 Results and Discussions
### Results of Introducing Confidence Levels
#### 6.1.1 Confidence Level \(\beta\)  
Table 3 reveals that with the rise of the confidence level \(\beta\), the system operational cost increases sharply, especially when \(\beta\) is closer to 1. Figures 4 and 5 show the optimal output power of different energy generators at \(\beta=0.8\) and \(\beta=0.9\), respectively.  
The value of the confidence level \(\beta\) represents the chance of achieving the spinning reserve constraints in the uncertain environment. With the decrease of \(\beta\), the spinning reserve for the system will be reduced and the gap of power exchange between the main grid and the microgrid will be increased. Figures 4 and 5 show that the chance constrained programming focuses on the operational cost optimization when electrical demands are light and the reserve demands are small. On the contrary, when electrical demands are heavy, the objective of the optimization changes to improve system reliability.  
Figure 4: The optimal output power of different energy generators at \(\beta=0.8\).  
Figure 3: The varied-curve surface diagram of operational cost.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{\(\beta\)} & \multicolumn{3}{c}{Operational Costs (\(\gamma\))} \\ \cline{2-4}  & \(\alpha\) = 0.8 & \(\alpha\) = 0.9 & \(\alpha\) = 1.0 \\ \hline
**0.8** & 2776.47 & 2926.02 & 3152.34 \\
**0.9** & 3866.33 & 4016.04 & 4502.28 \\
**1.0** & 5747.36 & 6038.09 & 7605.58 \\ \hline \end{tabular}
\end{table}
Table 3: System operational costs in different confidence levels.  
For example, when \(\beta=0.8\), the probability that total power generation of the microgrid is greater than the electrical demands is low. In this situation, the proposed distributed network takes the higher risk of reducing system stability and loss of loads. Meanwhile, the gap of power exchange between the main grid and the microgrid is relatively high. However, But on the other hand, the distributed system purchases power from the main grid at a lower price time and sells back at the peak price time, which improves the system's economy.  
When \(\beta=0.9\), the probability that the total power generation of the microgrid is greater than the electrical demand becomes slightly higher. In this case, the proposed distributed network takes the lower risk of loss of loads and increases system reliability. Additionally, compared with \(\beta=0.8\), the gap of power exchange between the main grid and the microgrid is reduced to some extent. Because of the large amount of spinning reserve prepared for the peak demand time and the conservation of selling electricity to the main grid, the system's economy can be affected.  
In summary, a lower confidence level \(\beta\) can lead to good system economy, while it can also result in higher risk of reducing system stability.