esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The MPwise System
### Embedded and Joint Processing on Node Level  
Different methods of analysis are under development and implementation, dependent upon the response time required by the application (e.g., real-time for early warning, near-real-time for rapid response), either using directly the capabilities of each single node that constitutes the self-organizing network, or in a more centralized early warning center. The processing steps at the node level are being designed to be suitable for on-site early warning and the assessment of the predicted damage of buildings, computer vision-based monitoring, displacement estimation by joint processing of single-frequency GNSS and MEMS-accelerometer data, and the running of an automatic, audio-visual alarming system (which would involve using either the embedded or external touchscreen display, external loudspeakers and/or other external devices). For example, Figure 3 (left), shows the processing steps for one of the EEW options, termed decentralized on-site early warning [5; 6; 7], and for the computer vision-based monitoring application (see Section 6). With regards to other sensor types that may be attached to the MPwise, an example could be the use of external GNSS units (see [14]), for example, the u-box GPS module ([[https://www.u-blox.com/de](https://www.u-blox.com/de)]([https://www.u-blox.com/de](https://www.u-blox.com/de))), which may be used in conjunction with the double-integrated acceleration data recorded by the internal MEMS (note, this GNSS module is in addition to the one used for the time stamping). In the following sections, we will present some examples of the different applications of the MPwise. One of the software tools installed on the MPwise is the GFZ-Sentry (GFZ-Sentry is being developed by GFZ in collaboration with gappaGmbH). This is a software application that runs in real-time and analyses the recorded ground motion for on-site and decentralized early warning. The software, which has been tested extensively on off-line recordings [7] exploits the first three-seconds of a triggered signal to determine (making use of empirical relationships) what the potential ground shaking at a site will be, which is then used to make a decision as to whether to issue an alarm (and of what type) [5; 6; 7].
## 3 System Performance Analysis
### Outage Probability at the Coupled Relays \(\mathcal{R}\)
 \tag{31}\]  
_where \(\beta_{i}\) is given by_  
\[\beta_{i} =\alpha_{i}-\gamma_{i}^{*}\sum_{j=1}^{i-1}\alpha_{j}, \text{for $i>1$}, \tag{32}\] \[\beta_{i} =\alpha_{i}, \text{for $i=1$}. \tag{33}\]  
See Appendix A for proof. **Remark 1**. : _If the network has a large number of devices, it is challenge to apply the expression (30) in Monte Carlo simulations. Fortunately, the authors of [12] analyzed a network model with multiple relays and multiple devices. The authors presented the expressions for OP at the relays as [12] (Equation (33)). It is important to mention that the present paper extends the work of the previous study [12] by deploying massive MIMO and TAS techniques. From [12] (Equation (33)), the OP expression at the coupled relay \(\mathcal{R}\) in (30) can be rewritten as follows:_  
\[ \begin{split} OP_{\mathcal{R}}^{\left(\Upsilon_{1}^{(\theta)} \right)}&=1-\prod_{i=N}^{1}\underbrace{\Pr\Biggl{\{}\max\Biggl{\{} \mathbf{R}_{\mathcal{R}-x_{i}}^{\left(\Upsilon_{1}^{(\theta)}\right)}\Biggr{\}} \geq\mathbf{R}_{i}^{*}\Biggr{\}}}_{\mathcal{Q}_{i}}.\end{split}  \tag{34}\]  
_From (34), we obtain a novel expression of OP at the coupled relay \(\mathcal{R}\) in closed-form by applying the Cumulative Density Function (CDF) as defined in [13] (Equation (71)):_  
\[ \begin{split} OP_{\mathcal{R}}^{\left(\Upsilon_{1}^{(\theta)} \right)}&=1-\prod_{i=N}^{1}\left(1-\sum_{\psi=0}^{A_{0}A_{1}} \frac{(-1)^{\psi}(A_{0}A_{1})!}{\psi!(A_{0}A_{1}-\psi)!}\exp\left(-\frac{\psi \gamma_{i}^{*}}{(1-\lambda)\beta_{i}\rho_{S}\sigma_{\mathcal{R}}^{2}}\right) \right).\end{split}  \tag{35}\]  
See Appendix B for proof.
# The Analysis of Periodic Order in Monolayers of Colloidal Superballs
## 3 Materials and Methods
### Analysis of the Colloidal Superball Monolayers Using SAXS  
The SAXS measurements were performed at the BM26B DUBBLE beamline of the ESRF in Grenoble, France using the microradian setup [23] employing compound refractive lenses [27]. The X-ray beam with a photon energy of \(12\) kV was focused on a CCD with a resolution of \(4008\times 2671\) pixels and pixel size of \(22\times 22\,\mathrm{\SIUnitSymbolMicro m}\) (Photonic Science) placed at a distance of \(7.5\) m from the sample. A wedge-shaped beam-stop was used in order to prevent damage to the detector that can result from an exposure to the direct X-ray beam. The colloidal monolayers were placed perpendicular to the X-ray beam with the rubbing direction along to the horizontal plane (\(x\)-axis). The samples were rotated around the \(x\)-axis from \(\phi=-60\) to \(60\). The SAXS setup is schematically depicted in Figure 2.  
Figure 1: Representative (**a–c**) transmission electron microscopy (TEM) images of the colloidal superballs and (**d–f**) scanning electron microscopy (SEM) images of their corresponding monolayers assembled using the unidirectional rubbing method. The direction of the mechanical rubbing is indicated in the images using a red arrow. The scale bars represent (**a–c**) 1 and (**d–f**) \(5\,\mathrm{\SIUnitSymbolMicro m}\).  
2D SAXS patterns were obtained at different rotations of \(\phi\) with an exposure time of 20 s. Dark current and background corrections were performed on the patterns before their analysis. The background images were obtained from a blank PDMS-coated glass slide at an equal rotation of \(\phi\). Every measured diffraction pattern image was subtracted with the background imaged at an equal rotation in the \(x\)-axis to correctly remove the scattering background that is caused by the PDMS-coated glass slide. Custom analysis scripts written in Python were used to determine the positions of the peaks in the cross sections of the diffraction pattern images and to numerically calculate the form factor of the different superball shapes. To model the form factor, we constructed the superballs, whether solid or hollow, on a three-dimensional raster with 30 points along each dimension. The interference between the points within the particles' shape was then calculated and converted via a Fourier transform.
## 4 Conclusions  
In this paper, we analyzed the principle of delay and sum beamforming in photoacoustic imaging. Meanwhile, on the basis of the multi-DASE method, we also analyzed the shortcomings of this method in imaging large-volume sources. In response to these shortcomings, we proposed a solution. Through verification, the method we proposed can effectively solve the problem of sudden changes in the signal, thereby avoiding the overshoot phenomenon that may occur in the envelope signal. The distortion problem of large-volume source imaging by multi-DASE method is solved. Our improved method retains the advantage of the multi-DASE method in suppressing artifacts while compensating for the problem of losing internal source information during imaging of larger volume sources. Additionally, our method also preserves the high parallelism advantage of DAS, making it applicable for real-time imaging. Simulation results verified the effectiveness of our approach. The proposed method is primarily designed for imaging sources with a certain volume, effectively addressing the limitations of the multi-DASE method. The photoacoustic imaging of large-volume tissues, such as the liver and breast, can help doctors observe the size, shape, boundaries, and internal structure of tumors more comprehensively. However, when applied to small-volume and complexly distributed biological tissues, such as blood vessels, the method exhibits certain limitations. Overcoming these limitations will be a key focus of our future research. Additionally, while the suppression method described in this paper is relatively straightforward, we plan to explore more sophisticated suppression techniques in subsequent studies to satisfy diverse imaging requirements. Furthermore, since we employed a relatively simple window function, its advantages were not particularly evident when dealing with some more complex problems. More suitable window functions and parameters can be explored to meet different application requirements and obtain higher-quality reconstructed images.  
Conceptualization, M.F. and X.J.; methodology, M.F. and X.J.; software, M.F., X.J., J.Z. and S.G.; validation, M.F.; formal analysis, M.F.; investigation, M.F. and X.J.; resources, X.J.; data curation, M.F.; writing--original draft preparation, M.F.; writing--review and editing, M.F., X.J. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 42174162.  
Not applicable.  
Figure 15: The imaging results of point-like sources. (**a**) A reconstructed image using the multi-DASE method; (**b**) a reconstructed image using the proposed method.  
**Data Availability Statement:** The data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
### Development of Polymorphic Genic SSR Markers  
by Transcriptome Sequencing in the Welsh Onion  
(_Allium fistulosum_ L.)  
**[PERSON] 1,2,\({}^{\dagger}\), [PERSON] 1,\({}^{\dagger}\), [PERSON] 1, [PERSON] 1, [PERSON] 1,**  
**[PERSON] 2,\({}^{\ast}\) and [PERSON] 1,\({}^{\ast}\)**  
\({}^{1}\) Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Sciences/  
Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China),  
Ministry of Agriculture, Beijing 100097, China; E-Mails: [EMAIL_ADDRESS] (L.Y.);  
[EMAIL_ADDRESS] (C.W.); [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (Q.L.);  
[EMAIL_ADDRESS] (J.Y.)  
\({}^{2}\) College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\ast}\) Authors to whom correspondence should be addressed;  
E-Mails: [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (Y.W.);  
Tel.: +86-71-6806-6264 (L.L.); +86-10-5150-3125 (Y.W.);  
Fax: +86-71-6806-0812 (L.L.); +86-10-5150-5002 (Y.W.).  
Academic Editor: [PERSON]  
_Received: 14 September 2015 / Accepted: 23 October 2015 / Published: 5 November 2015_  
**Abstract:** Transcriptome analysis is an efficient way to explore molecular markers in plant species, for which genome sequences have not been published. To address the limited number of markers published for the Welsh onion, this study found 6486 loci of genic simple sequence repeats (SSR), which consisted of 1-5 bp repeat motifs, based on next-generation sequencing (NGS) technology and the RNA-Seq approach. The most abundant motif was mononucleotide (52.33%), followed by trinucleotide (31.96%), and dinucleotide (14.57%). A total of 2525 primer pairs were successfully designed, and 91 out of 311 tested primers were polymorphisms. Overall, 38 genic SSR markers were randomly selected to further validate the degree of genetic diversity, and 22 genic SSR markers (57.89%) showed high levels of polymorphism. The average polymorphism information content (PIC) value and the number of alleles (Na) were 0.63 and 5.27, respectively, and the unweighted pair-group method with arithmetic average (UPGMA) cluster analysis grouped the 22 _Allium_ accessions into three groups with [PERSON]'s similarity coefficients ranging from 0.37 to 0.99. This result suggested that these genic SSR markers could be used to develop a higher resolution genetic map and/or to analyze the phylogenetic relationships among _Allium_ plants in the near future.  
simple sequence repeats (SSR); genic marker; transcriptome; genetic diversity; _Allium fistulosum_ L.
## 2 The Measurement Partitioning Problem
### The Measurement Partitioning Algorithm Based on Distance  
A distance-based measurement partitioning algorithm (i.e., so-called distance partitioning) was proposed. It utilizes the statistical properties of distances between measurements obtained from the same target to reduce the number of partitions. However, the computational burden still grows rapidly in many cases when the number of targets increases. Furthermore, the distance-based underlying mechanism that measurements generated from the same target are close to each other will no longer applicable to the spatially close target tracking scenario. In this case, all the measurements are densely distributed in the same region once targets make a cross with each other. One clustering of the measurements may be generated from more than one extended target. However, distance partitioning is very likely to mistreat this clustering from one target, causing a cardinality error in the following filtering. In this point, the sub-partitioning algorithm was proposed on the basis of distance partitioning to better handle the estimated problem of cardinality.  
The sub-partitioning is an augmentation of distance partitioning, which repartitions the subsets containing measurements from multiple targets by distance partitioning. It uses K-means++ clustering to repartition the subset \(W_{j}^{i}\) into \(N_{x}^{i}\) smaller subsets if the maximum likelihood (ML) estimation about the number of targets for each subset is larger than 1. These smaller subsets are added to the list of partitions, which are considered and utilized by the ET-PHD filter framework. However, this sub-partitioning strategy only focuses on improving the estimation performance of target number without considering the heavy computational burden, which is not good for real-time target tracking. Moreover, the cardinality error still exists, especially in the tracking scenario where four or more targets are being crossed. This is mainly because the loss function of the K-means++ clustering causes inaccurate repartitioning. These inaccurate repartitioning results will not contribute much to the target tracking due to the lower likelihood obtained by these partitions compared with one obtained by the relatively accurate partitions.
###### Abstract  
Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data provides four statistical measures (mean, standard deviation, maximum value, and minimum value) of hundreds of wind turbine magnitudes, usually in a 5-min or 10-min interval. Several studies have analysed the loss of information associated with the reduction of information when using five minutes instead of four seconds as a sampling frequency, or when compressing a time series recorded at 5 min to 10 min, concluding that some, but not all, of these magnitudes are seriously affected. However, to our knowledge, there are no studies on increasing the time interval beyond 10 min to take these four statistical values, and how this aggregation affects prognosis models. Our work shows that, despite the irreversible loss of information that occurs in the first 5 min, increasing the time considered to take the four representative statistical values improves the performance of the predicted targets in normality models.  
wind turbine; fault diagnosis; renewable energy; extreme learning machines (ELM); feature engineering; normal behaviour models Article  
Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis  
1
## 3 Results and Discussion
### Optimization Results Using PSO without and with DG integration
#### 3.2.1 Scenario One: Base Case  
The results obtained for the base case condition where no DG was integrated into the system, using the PSO method, are presented in Table 3. The results after 200 iterations showed that the maximum and minimum voltages were associated with buses 1 and 18, whose voltage magnitude values were 1.000 pu and 0.8297 pu, respectively.  
It can be seen that buses 18, 30, 31, 32 and 33 with voltages 0.8904, 0.8737, 0.8447 and 0.8403, respectively, were outside the prescribed voltage limits of 0.95-1.05 pu. Also, the total power loss obtained through the power-flow analysis using the forward/backward sweep iteration method without the integration of DG was 1.0268 MW. The voltage variation graph for this scenario is presented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Bus No.**} & **Voltage** & **Voltage** & \multirow{2}{*}{**Bus No.**} & **Voltage** & **Voltage** \\  & **(pu)** & **Ranking** & & **(pu)** & **Ranking** \\ \hline  
& \\ \hline \hline \end{tabular}
\end{table}
Table 2: Voltage profiles for identifying weak buses in the IEEE 33-bus distribution system.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Number of Iterations**} & **200** \\ \hline
**DG location** & nil \\
**Minimum voltage** & 0.8614 pu \\
**Maximum voltage** & 1.0000 pu \\
**Network real power** & 3715 kW \\
**Network reactive power** & 2290 kWar \\
**Power loss without optimization** & 1.0268 MW \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results when no DG was integrated into the system.
## Numerical Simulation and Parametric Analysis
### Discussions
#### 4.4.6 Effect of the Diameter and Shape of Steel Sheets  
Figure 20 shows the influence of the diameter and the shape of steel sheets on the ultimate compressive resistance and DI ratio of the SDC wall. It can be found that increasing the diameter leads to slight increases of the ultimate compressive resistance and initial stiffness. This is because the changes of the cross-sectional area of the steel sheets are marginal compared to the cross-sectional area of the wall.  
However, it can be indicated that the diameter of steel sheets has an obvious influence on the ductility of SDC walls. The DI ratios are increased by 9.3% and 36.4% as the diameter increases from 15 mm to 25 mm and 35 mm. This can be explained that steel sheets can provide lateral restraint on the concrete core and thus inhibit the development of transverse cracks in the concrete after the concrete reaches the compressive strength. The steel sheet with a larger diameter can provide a larger area of constraints on the concrete.  
It can also be found that changing the shape of steel sheets from circle (diameter = 25 mm) to square (side length = 25 mm), the ultimate compressive resistance and DI ratio increases moderately. This is because the increased area of steel sheets provides more confinement on the concrete core. However, compared to a circular sheet, when the wall is under pressure, the square sheet is more  
Figure 19: The relation between normalized strength and plate slenderness (s/ts).  
Figure 18: Influence of spacing of binding bars on compressive resistance of SDC wall.  
prone to undergo stress concentration at its corners and to scratch the rubber layer. Hence, considering the mechanical properties and durability of the structure, the circular steel sheet with a diameter of 25 mm is utilized in the SDC wall.
## 4 Discussion
### Computational Cost  
The computational cost has always been a primary concern in compressor design optimization. The computation consumption of the RL agent and GA process was considered in this section. Since the surrogate models accounted for most of the computational cost, it was appropriate to evaluate different algorithms' costs in training and execution by counting the calling time of surrogate models.  
The convergence history when training the agent was plotted in Figure 24, where the mean line was calculated by all R curves still in training. The training process was repeated several times to reduce the influence of randomness in network initialization and noise action. The mean R increased before 5000 episodes, and many agents converged and stopped training, so the mean R decreased at 5000-6000 episodes. One episode contained 100 steps, so 5000 episodes corresponded to 500,000 calls of the surrogate model, approximately two times that of the NSGA-II evolutionary process and on the same order of magnitude. The agent learned the policy of the whole design process with the same order of magnitude of computational cost as NSGA-II learning a Pareto front.  
Figure 23: Entropy distribution of the meridional velocity at 50% and 80% of the axial length.  
The trained agent showed its advantage in generating the new design because of the different running logic. Agents could modify all designs in the design space and improve the performance designers expect, with only one calling surrogate model for each step. The modified design variables changed continuously.  
For the GA, the Pareto front had no straightforward relations to other points in the design space. The nearby points in the Pareto front could have entirely different design variables. Therefore, another evolution process needed to be performed with different fitness functions and extra constraints to generate modification steps similar to those of the agents. Thus, the agents yielded modifications much faster than the GA. As described in Section 3.2, one GA step consumed 14,705 times the calling times of surrogate models as 17 RL steps.  
In summary, training agents had a higher computational cost than the GA, but the trained agents produced modifications much faster. It was appropriate to initialize the RL design process at the GA results rather than initiate at the reference or random design states and then modify the rotor using trained agents to obtain rotors that met the requirements.
## 2 Results and Discussion
### Structure Determination  
Base-triggered condensed product **9** can exist as several tautomers (Scheme 2). To determine the structure of the tautomer present in solution, NMR spectra were measured both in DMSO-d\({}_{6}\) and CDCl\({}_{3}\), and they exhibited analogous patterns. The proton NMR spectrum of **9**[1,2]** showed two methylene singlets (\(\delta\) = 4.43 and 3.99) and one singlet corresponding to an olefinic proton (\(\delta\) = 6.15). These resonances indicated the presence of tautomer **9b** or **9c**. The \({}^{13}\)C NMR spectra did not show the presence of a ketone carbon (\(\delta\) = 205.0 in the case of compound **8**), eliminating tautomer **9c**. Analogous findings were observed with compound **9**[5,3], which exhibited one diagnostic olefinic singlet and two quartets coupled to two methyl groups, suggesting the same type of tautomer **9b**. The LC/MS analysis and NMR spectra of **9** also did not indicate the presence of any diastereomer, confirming that the optical integrity of the amino acid chiral carbon was preserved.