esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments Results and Discussion
### Performance on Automatic Segmentation Task  
Our proposed framework WDTIseg could both obtain automatic segmentation and refine results based on interactions. To demonstrate that our method did not require an additional training of an automatic segmentation network to obtain the initial segmentation, we compared the automatic segmentation results of U-net and WDTIseg.  
Table 1 shows automatic segmentation results of U-net and WDTIseg. The dice of automatic segmentation results of WDTIseg was 82.86 \(\pm\) 16.22 (%), better than that of U-net. In the automatic segmentation examples in Figure 5, it is clear that the results of WDTIseg were similar to U-net, and the segmentation results were even slightly more compact.  
These prove that WDTIseg can still have comparable automatic segmentation performance to U-net after interactive segmentation training.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Dice (\%)** \\ \hline U-net (automatic) & 80.50 \(\pm\) 18.78 \\ WDTIseg (automatic) & 82.86 \(\pm\) 16.22 \\ \hline Graph cuts & 82.46 \(\pm\) 15.53 \\ Random walker & 85.16 \(\pm\) 13.85 \\ DeepIGeoS (R-net) & 92.49 \(\pm\) 7.05 \\ \hline WDTIseg (\(\lambda=0,\ \mathcal{L}_{\text{total}}\)) & 92.28 \(\pm\) 7.13 \\ WDTIseg (\(\lambda=0.25,\ \mathcal{L}_{\text{total}}\)) & 93.89 \(\pm\) 4.70 \\ WDTIseg (\(\lambda=0.5,\ \mathcal{L}_{\text{total}}\)) & 94.45 \(\pm\) 3.26 \\ WDTIseg (\(\lambda=0.75,\ \mathcal{L}_{\text{total}}\)) & 93.54 \(\pm\) 3.63 \\ WDTIseg (\(\lambda=1,\ \mathcal{L}_{\text{total}}\)) & 92.87 \(\pm\) 6.09 \\ WDTIseg (\(\lambda=0.5,\ \mathcal{L}_{\text{Dice}}\)) & 92.17 \(\pm\) 7.29 \\ WDTIseg (\(\lambda=0.5,\ \mathcal{L}_{\text{Dice}}\)) & 93.01 \(\pm\) 6.46 \\ WDTIseg (\(\lambda=0.5,\ \mathcal{L}_{\text{Dice+ICE}}\)) & 93.54 \(\pm\) 3.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of dice values of the final segmentation results after 8 interactions. Automatic segmentation results of U-net and WDTIseg are presented to reflect the gain of the interactive segmentation methods.  
Figure 5: The automatic segmentation results of U-net and WDTIseg.  
Our study focused on improving automatic segmentation-based refinement, and for the first time, we proposed that the interactive segmentation network can generate the initial segmentation results by itself without the need to train additional automatic segmentation networks.
## Feasibility Study of Energy Storage Systems in Wind/Diesel Applications Using the HOMER Model  
**[PERSON] and [PERSON] ***  
School of Chemical Sciences and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 27 August 2012; in revised form: 24 September 2012 / Accepted: 25 September 2012 / Published: 18 October 2012_  
**Abstract:** With an increased focus on solutions to the ensuing \"climate crisis\", the need for energy storage systems is becoming increasingly important as a means to increase the penetration of renewable technologies such as wind energy. The Vanadium Redox Battery is one such energy storage system showing considerable potential owing to its flexibility in power output and capacity, high efficiency and long operating life. This study models the use of the Vanadium Redox Battery as an integration technology in realistic large-scale remote wind/diesel power systems using the HOMER Micropower Optimization Model computer program developed by the US National Renewable Energy Laboratory. Results from this modelling demonstrate the significant financial and environmental benefits to be gained in installing energy storage in a wind farm. The storage system considered here was a Vanadium Redox Battery.  
**Keywords:** energy storage; vanadium battery; wind energy storage; simulation
# Periodic Multilayer for X-ray Spectroscopy in the Li K Range
## 3 Results  
Figure 2 shows the 0-20 reflectivity curves obtained for six photon energies from \(\sim\)47 to \(\sim\)57 eV. Reflectance is quite large, around 0.3. As expected from the Bragg law, the position of the peak shifts toward low angles when the photon energy increases. The increase in reflectivity with increasing radiation energy is associated primarily with a decrease in the absorption of all materials present in the stack.  
Figure 1: Angular dependence of the reflectance at the wavelength of 0.154 nm: experiment, points; fit, solid line.  
The reflectivity curves measured as a function of photon energy, from 40 eV up to 195 eV, for glancing angles corresponding to five maxima of angular curves of Figure 2 are shown in Figure 3. The first order diffraction peak is observed around 50 eV, after which the reflected intensity decreases steadily. There are no clear high order diffraction peaks; this confirms the advantage of using the three selected materials. Large variations of the reflectance are observed in the range of the Al L2,3 (around 73 eV), Si L2,3 (around 99 eV) and Be K (around 112 eV) absorption edges, related to drastic changes of the optical indices of the corresponding elements in these photon energy ranges.  
Figure 3: Reflectivity curves (in log scale) of the Be-based multilayer as a function of the incident photon energy for different glancing angles corresponding to five maxima of Figure 2. The vertical bars mark the position of the Al, Si and Be absorption edges.  
Figure 2: Reflectivity curves (in linear scale) of the Be/Si/Al multilayer as a function of the glancing angle for different photon energies spanning the range of the Li K emission.  
For the five curves of Figure 3, Table 1 gives the bandwidth, determined as the full width at half maximum of the first order diffraction peak, and the rejection rate, determined as the ratio of the reflectance measured at the maximum of the peak (R1) to the reflectance measured at twice (R1/R2) or three times (R1/R3) this energy. The reflectance values are the ones determined at the maximum of the angular reflectivity curves in Figure 2. The rejection of the third order diffracted radiation is about ten times more efficient than the rejection of the second order.
# Research on Permanent Magnet Synchronous Motor Control System Based on Adaptive Kalman Filter
## 4 Conclusions  
To address the inaccuracy statistics process noise problem in the process of PMSM speed estimation of EKF, this paper proposes a new linear time-invariant PAEKF algorithm. The algorithm uses the posteriori sequence to predict the prior error covariance adaptively, and solves the problem of the strict restriction of the Kalman filter to the noise covariance matrix. Compared with the trial-and-error method, the method proposed in this paper saves the work of obtaining the optimal noise parameters and speeds up the development process in practice. Finally, this paper compares the proposed algorithm with the traditional EKF and the existing adaptive EKF. The simulation results show that the proposed algorithm can estimate a more accurate real-time speed of the motor, effectively reduce steady-state jitter, and improve the adaptability and the calculation accuracy of the filter.  
The motor parameters (stator inductance, stator resistance, rotor flux) will change with its operation. In order to make the calculation of PAEKF more accurate and the research more complete, our future work is to think about how to complete the self-renewal of motor parameters from the inside, further improve the accuracy of sensorless algorithm, and apply it to the field of automotive electric assisted steering.  
Conceptualization, J.C. and Y.H.; methodology, J.C.; software, W.X.; validation, J.C., Y.H., W.X. and X.Z.; formal analysis, X.L.; resources, J.C.; data curation, W.X.; writing--original draft preparation, W.X.; writing--review and editing, J.C., H.Q. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This paper is funded by National Key R&D Program of China (2019 YFC1711902).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
The proposed OW-DCN combines the benefits of the high data rate and format signal transparency of optical switching with optical wireless technology to enable fast optical packet-switching operation, reduce the large power-consuming O/E/O conversion of traditional electrical-switch-based data centers [23], and benefit from all the above-mentioned wireless properties. For this OW-DCN, a detailed packet switching strategy, contention-solving methodology, and reconfiguration capability are elaborated. To be specific, a SWS that is placed on top-of-the-rack (ToR) switches controls a group of SOAs for quickly selecting/tuning the transmitted wavelength of the transceiver in a few nanoseconds. A passive AWGR and an FPGA-based switch scheduler comprise the intra- or inter-cluster optical packet switch for the intra- and inter-cluster interconnection between racks. By appropriately changing the central wavelength of the SWS at the ToR switch, the data can be rapidly switched from any input port of AWGR to any output port, thus reaching any of the target ToRs. Meanwhile, the FPGA-based switch scheduler is implemented for resolving the possible contention to ensure a fast packet switching scheduler, while the SDN-based control plane is employed for look-up table deployment, monitoring, optimizing, and reconfiguring the network topology (via the distribution of new lookup tables to the ToRs and schedulers) to support the dynamic traffic demands. A functional plane of the FPGA-based switch scheduler with the label system for priority assignment, label processing, contention solving, acknowledgment, and retransmission was experimentally verified. Next, the reconfigurability of this architecture was confirmed with the SDN-control plane implemented by OpenDaylight (ODL) and Openstack platforms. For the data plane, 50 Gb/s transmission performance of traffic between ToRs is evaluated via bit error rate measurements for all the validated combinations. Moreover, the switching performance with ToR-to-ToR true packet-switch operating at 50 Gb/s was also experimentally demonstrated, which validates the architecture's packet-switching and delivery credentials. This paper is organized as follows. Section 2 describes the novel OW-DCN architecture and the system operation of the SWS-based ToR switch, AWGR-based optical switch, and SDN-based reconfiguration. The validation and assessment of contention solving, reconfiguration via priority assignment, and performance of data transmission and switching are reported in Section 3. Section 4 concludes the paper by summarizing the main results.
# Optimization-Based Input-Shaping Swing Control of Overhead Cranes
## 1 Introduction
Specifically, the pulse response sequence of the closed-loop system was identified and fitted using an online frequency ELM algorithm, and the shaper parameters were updated in real time. Although the above adaptive input-shaping techniques have achieved a certain performance in payload swing suppression and system robustness, they have some disadvantages, such as long optimization times and the inability to accurately optimize the parameters in some cases. Additionally, the PSO algorithm has the characteristics of a simple structure and fast optimization process and can effectively deal with the problem of modeling errors in crane swing control. However, it is limited to real-time disturbance. Based on the previous analysis and discussion, a swing control scheme based on PSO and adaptive techniques for overhead cranes is proposed in this work, and its contributions are summarized as follows:  
1. Different strategies are used to deal with system uncertainty. The offline PSO method is adopted for the modeling error, and the online adaptive method is adopted for the real-time disturbance, which ensures the performance of swing control. 2. The search ability of the particle swarm optimization algorithm is ensured. A time-varying inertia weighting coefficient is used, which has a large value and a slow change rate in the early stage but has a small value and a fast change rate in the later stage. This coefficient makes the global and local search capabilities of the particle swarm optimization algorithm well balanced. 3. The method for the online optimization of the shaper parameters is unique. By adding a feedback link and setting an additional shaper, the optimization of the original shaper parameters is transformed into an adaptive iterative optimization problem of additional shaper parameters, thus avoiding the difficulties encountered in the direct optimization of the original shaper parameters. The rest of this work is organized as follows. In Section 2, the model of an overhead crane is introduced. Section 3 states the design of the shapers using the analytical method and the optimization of the shaper parameters using the PSO algorithm. The adaptive iter ative optimization of the shaper parameters is presented in Section 4. Section 5 summarizes this work.
The recovery of image details and the removal of stray light noise need to be improved. Image super-resolution (ISR) processing algorithms can increase the resolution of an imaging system without upgrading the imaging system hardware. Fusion of the single-frame image ISR algorithm into the computational imaging module can improve imaging systems' performance. Deep learning models have become more widely used in ISR algorithms because they offer significant improvements over conventional algorithms [16; 17; 18; 19; 20; 21; 22; 23]. Recent ISR Networks employ a residual learning framework [17; 18] to learn high-frequency image details, which are then added to the input low resolution(LR) images to produce super-resolution images. Data-driven deep learning network model ISR algorithms vary in their architectural design. Other network techniques that produce ISR images include compressed sensing [19], densely connected networks [20], Laplacian deep convolutional networks [21], multiple attention mechanisms [22], and generative adversarial networks (GAN) [23]. In this study, we analyzed HDOE focusing performance in wide-bandwidth spectral applications. The imaging degradation caused by chromatic aberration and diffraction efficiency reduction in HODE wide-spectrum imaging is analyzed. We described improvements made to the computational imaging module by combining the image restoration algorithm with the image super-resolution algorithm. We design a DDZMR network for simultaneous image restoration and image super-resolution, and customize a multimodal objective function based on the HDOE image quality degradation model. We also built a demonstration prototype of the application of a SHDCIS in the visible spectrum, as shown in Figure 1. The results of the experiments show that the imaging capability of the prototype can reach the level of photographic imagery and it produced satisfactory results for deblurring, correcting chromatic aberration and ISR.
# Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
## 5 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & _PCC (\%)_ & _KC_ \\ \hline NSCTKFCM & 82.63 & 0.5527 \\ NSCTFCM & 84.51 & 0.7816 \\ PCANet & 90.78 & 0.8569 \\ DNN & 91.57 & 0.8932 \\ MRFFCM & 88.34 & 0.8544 \\ L-V-NSCT & 92.96 & 0.8845 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average results of change detection by different methods. Figure 6: Overall changes under large scale conditions. (**a**) remote sensing images of Dongting Lake from January 1973, (**b**) remote sensing images of Dongting Lake from February 2014, (**c**) the changes in Dongting Lake on a large scale, and (**d**) the region with the greatest change in the map.
## 2 Materials and Methodology
### Effect of Relative Humidity (40%, 50%, and 60%)  
To evaluate CO2 capture under different relative humidity environments, the recycled aggregates (MRA, RCA, and CA-L) were submitted to forced CO2 carbonation at various exposure times in a climatic chamber with the following conditions: 60 \({}^{\circ}\)C, 25% CO2 with 40%, 50%, and 60% RH. Figure 10 displays the results obtained for the tested RA. Looking at the general trend of the results obtained, it is possible to conclude that the decrease in relative humidity to values below 60% impaired CO2 capture by the RAs studied. This result is in accordance to other works in the literature [53; 54; 55]. The percentages of CO2 captured per cement paste range from 2.5% to 34.8%, with the highest capture rate (34.8%) observed in RCA IW at 60% relative humidity.  
The results indicate that, when the relative humidity drops below 60%, there is a negative impact on CO2 capture, regardless of the amount of cement paste available in the RA, with the exception of two cases (MRA-RP3 and RCA-RP2).  
As demonstrated in the research of [PERSON], the utilisation of RH values that fall below the optimal range is likely to yield substandard water content, which in turn exerts a substantial influence on the reaction between CO2 and cementitious materials in RA. However, at levels that exceed the optimal value, the RA microstructure is susceptible to saturation because of excess water, which leads to pore clogging and subsequent inhibition of carbonation.  
As demonstrated in the work of [PERSON] et al. [55], the use of RH below the optimal value will result in poor water content, which has a crucial impact on the reaction between CO2 and cementitious minerals in RA. Also, at levels that exceed the optimal value, the RA microstructure is susceptible to saturation due to excess water. This saturation leads to pore clogging, which can inhibit carbonation. In conclusion, maintaining humidity levels around 60% is essential for effective CO2 capture, regardless of the amount of cement paste in the recycled aggregates. These results are consistent with the range defined in the literature for \"optimal\" relative humidity to achieve a higher carbonation rate [7]. In general, reducing relative humidity resulted in less CO2 absorption in MRA, RCA, and CA-L (Table 7). The decreased water supply hindered the efficiency of this reaction, leading to reduced CO2 capture. Interestingly, not all cases showed an increase in the time taken  
Figure 9: Weight loss due to decarbonation before and after forced carbonation, considering the time at which maximum CO2 uptake was achieved for CA-L at 25, 18, and 10%.  
for maximum carbonation to occur. In some instances, the period of carbonation remained the same, suggesting that, while lower humidity affected the CO\({}_{2}\) uptake of RA, it did not universally prolong the reaction time. The observed decrease in CO\({}_{2}\) uptake with reduced humidity is in accordance with the literature regarding cementitious material carbonation.
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 2 ChG Glasses and Fibers
### Overview of ChG Fibers
#### 2.2.3 Tapered Fibers  
As a well-known post-processing technology, tapering fibers can also dramatically increase the optical nonlinearity and engineer the total dispersion of fibers. However, tapering is an easier technical task compared with MOFs in terms of fabrication difficulty. The typical methods used to taper ChG fibers include flame brushing [92] and electric heating [93], and a low tapering loss of 2 dB has been achieved [94]. The schematic of a typical ChG tapered fiber is shown in Figure 3.  
During the tapering process, material dispersion is compensated by waveguide dispersion by reducing the fiber diameter, which results in the blue-shifting of ZDW. A small fiber core size corresponding to a large \(\gamma\) of 1.85 \(\times\) 10\({}^{5}\) W\({}^{-1}\)km\({}^{-1}\) has been reached in an As-Se fiber taper with a taper waist diameter of 0.47 \(\upmu\)m [95], a value that is approximately tens to hundreds of times than that of a ChG step-index fiber with a large mode area. By combining tapering with MOFs, a relatively greater nonlinearity can be obtained. Meanwhile, the threshold power required for nonlinear effects is relatively low. Self-phase modulation (SPM) can occur at a peak power lower than 1.5 W in a submicron As\({}_{2}\)Se\({}_{3}\) taper [96].  
Figure 3: Schematic of a typical ChG tapered fiber.  
Figure 2: Geometrical profiles of ChG microstructured optical fibers (MOFs). (**a**) Photonic crystal fiber with regular hexagonal geometry and three air hole rings; (**b**) sSuspended-core fiber with three air holes; (**c**) suspended-core fiber with six air holes (from [79]); and (**d**) hybrid MOF (reproduced with permission from [80], copyright Optical Society of America, 2014).
## 4 Results and Analysis
### Effect of Network Parameters on Welfare of P2P Platform
#### 4.5.2 Sensitivity towards Loss Coefficient  
Figure 16 shows the effect of the loss coefficient on the prosumers' welfare for Interval I. It is evident from Figure 15(a) that if the value of the loss coefficient (\(\beta_{ij}\)) was increased  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Trading Method**} & **PV Trading for** & **PV Trading for** & **Monthly Customers** & **Monthly Prosumers** \\  & **Interval I (\%)** & **Interval II (\%)** & **Savings (\$)** & **Savings (\$)** \\ \hline ZI Method & 74.78 & 22.79 & 35.05 & 29.10 \\ GT Approach & 71.04 & 21.34 & 32.50 & 27.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage of local trading of P2P energy for the two intervals and average monthly savings.  
Figure 15: Effect of the contribution factor on the welfare of the participants for Interval I. (**a**) ZI strategy. (**b**) GT approach.  
to a high value, the welfare of the prosumers became negative. This was due to the fact that, for very high values of \(\beta_{ij}\), the majority of the PV power of the prosumers was utilized in meeting the losses of the system. A small portion of the P2P power traded by the prosumers was utilized by the customers, which resulted in a negative revenue compared to the traditional market. In the ZI strategy, because the prosumers do not have any preference when determining the amount of traded energy for the matched orders, the P2P energy will remain the same for each value of the loss coefficient. Since \(P_{L}=\beta_{ij}\mu_{ij}^{2}\) at constant value of traded energy \(t_{ij}\), increasing \(\beta_{ij}\) increases the trading losses \(P_{L}\). Contrarily, for the GT approach, the prosumers can use their reluctance parameter \(\alpha_{j}\) to limit their participation in the P2P market by decreasing the \(t_{ij}\) under heavy distribution losses.  
Figure 16b shows the trading pattern of the prosumers for different values of the loss coefficient using the GT approach. For high values of the loss coefficient, the prosumers increased their reluctance parameter \(\alpha_{j}\). This resulted in the limited amount of PV energy traded with the customers that could avoid the negative welfare for the prosumers. Under such circumstances, the prosumers preferred to sell their surplus PV energy to the operator as an FIT. This is one of the main advantages of the suggested GT approach, where the prosumers can adjust their trading pattern based on the network parameters using the parameter \(\alpha_{j}\).