esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Taking advantage of the experience gained during the SAFER and EDIM projects, we developed a new, dedicated system for seismic arrays, named the GFZ WIreless SEismic array (GFZ-WISE) made up of a large number of low-cost Wireless Sensing Units (GFZ-WSU), which allow dense 2D seismic ambient-noise arrays to be deployed. We verified that the MEMS accelerometric sensors used by the SOSEWIN sensors do not have the sufficient resolution for seismic noise measurements and analysis. Therefore, for such a specific task the GFZ-WSUs are equipped with passive external geophones. Innovatively, the GFZ-WISE system will create a self-organizing wireless mesh network that will be capable to flexibly adapt to broad range of users and unforeseen network development, as, for instance, if changes in the network configuration will occur for the increase of the sensor number, or decrease if some of them will fail. During seismic noise investigations, these arrays will allow the real-time retrieval, _via_ the SeedLink protocol [16], and analysis of data for the rapid estimation of the local S-wave velocity structure. In the framework of site monitoring activities, the GFZ-WISE might be exploited to continuously estimate the subsoil mechanical properties, for example of landslides. In particular, it could allow, through the joint analysis of the multi-parameter data (e.g., the S-wave velocity, ground motion, groundwater level variation, and rain gauge) and dedicated decision making algorithms, to detect and locate changes within the landslide, and also provide real time early warning information about the possible landslide activation after earthquakes or meteorological events. On the other hand, in the urban context the GFZ-WISE system could be used for monitoring the variation of the subsoil mechanical properties following the shaking of an earthquake and to study the soil-structure interaction effects. In this paper, we first describe the general philosophy of the GFZ-WISE, as well as the hardware and software characteristics of the WSUs. Then we report on the laboratory and field tests performed, in particular the field experiment using the GFZ-WISE within the Alfred Einstein Science Park, Potsdam, Germany.
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 5 Conclusions  
One crucial parameter must be estimated carefully when an earthquake occurs: the earthquake's magnitude. Underestimating an earthquake's magnitude will result in an inaccurate early warning and emergency response. This paper proposed a method using a GA to estimate four source parameters (i.e., length, width, rake, and slip). The proposed method can also estimate the moment magnitude with greater reliability and accuracy than previous methods, at least for the earthquake case analyzed in this study: the 2011 Japan Tohoku Oki earthquake. However, this method currently only estimates four source parameters and needs prior information to carry out the inversion. Hence, the model proposed in this study should be improved in future work.  
Conceptualization, A.N. and I.M.; methodology, A.N., I.M. and C.M.; software, A.N.; validation, C.M., S.W. and I.M.; formal analysis, A.N.; resources, A.N., I.M. and S.S.; writing--original draft preparation, A.N.; writing--review and editing, C.M., S.W., A.N. and I.M.; visualization, A.N. and S.S.; supervision, C.M., S.W. and I.M.; project administration, A.N. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Institute of Technology Bandung (ITB) through the ITB competitive Research Program, grant number LPPM.PN-6-09-2021 and Indonesia Endowment Fund for Education (LPDP), grant number PRJ-102/LPDP/2021.  
The authors declare no conflict of interest.
## 4 Discussion
### Limitations of Study  
This study comes with some limitations. First, since this research endeavor pursued a case study paradigm with a focus on qualitative data collection, the unique operational contexts of the living labs investigated may not fully represent the diverse challenges and setups of automated shuttle systems globally. It is suggested that future research takes a more holistic approach, examining a broader variety of automated shuttle systems. This could also include a systematic consideration of external factors, e.g., passenger behavior, traffic conditions, and weather, on shuttle operations. The results from this study may provide a starting point for this.  
Second, this study relies on the perspectives of a small number of current operators in specific living labs within a distinct regulatory and cultural context. Even though the operators' perspectives cover months or years of continuous exposure to and experience with the shuttle and on-board operator HMI systems, this experience may not be representative of operators with varying levels of technical proficiency or from different cultural backgrounds. Including a broader demographic of operators as well as additional operational contexts in future research could yield additional insights into the usability and ergonomic design of on-board operator HMIs and facilitate a global application.  
Third, as the chosen approach is based on on-board operators' self-report data, no objective measurements of operator performance and system usability were conducted. Integrating quantitative data, such as reaction times, error rates, and physiological measures (e.g., stress levels), may help to assess and compare on-board operator HMIs in a more valid and reliable manner. Suitable methods may encompass systematic interview studies with users of automated shuttles [39] and eye-tracking paradigms [40].  
Fourth, even though the operators interviewed in this study had considerable experience in overseeing their respective automated shuttle systems, this study collected data only once, and therefore does not account for the long-term impact of ergonomic and HMI design on operator health and system efficiency. Longitudinal studies tracking changes in operator well-being, system trust, and performance over time would provide valuable data on the feasibility of current HMI designs.  
Fifth, it has to be noted that although the investigated workspaces and HMIs for on-board operators were state of the art at the time of the interviews, rapid technological advancements may have an impact on on-board operator workspace design. Thus, this study's findings may need to be reviewed in future research in order to stay relevant. Follow-up studies should explore state-of-the-art HMI designs longitudinally at regular intervals, including adaptive HMIs, along with other new developments.
## 2 Materials and Methods
### Data Analysis  
Baseline characteristics of participants were described using the mean and standard deviation for quantitative variables and absolute frequency and percentages for qualitative variables. Associations between the continuous variables of cardiovascular autonomic function and inflammatory biomarkers at baseline (in both groups and only in the infection group) were estimated using the Spearman's Rho Correlation Coefficient. Furthermore, to study the correlation between HRV and blood cytokines, repeated measures correlation were performed. The within-subject correlation coefficients are presented to account for the correlation between the changes in both assessed variables. Analyses were performed using STATA/SE version 16.1 (StataCorp, College Station, TX, USA), two-sided \(p\)-values were used and the statistical significance threshold was set a priori at 0.05.
## 3 Full Description of the Approach
### Reconstruction of Structural Elements
#### 3.3.2 File Generation into IFC Format  
The obj format is not a BIM format. Indeed, only the geometry of structural elements is described in a such a file. Neither object class is assigned to elements contrarily to what it is expected in a building information model. A conversion phase is thus necessary to be able to integrate results in BIM software. The Industry Foundation Classes (IFC) format was chosen as format for the output of the approach. The IFC format is a standardized object-based file format used by the AEC industry to facilitate the interoperability between the building actors. Both elements and relationships between elements are described in this type of file. It has the advantage not to be a proprietary format.  
In order to generate a file in IFC format from a file created in obj format, the open-source parametric 3D CAD modeller FreeCAD was used. The Architecture module is particularly interesting since it provides BIM workflow to FreeCAD, with support for features like IFC support (thanks to IfcopenShell, an open source library), fully parametric architectural entities such as walls, structural elements or windows.  
Figure 8: Reconstruction of slabs and walls—1 st floor of the INSA building.  
The FreeCAD software allows importing the created obj file. Once the file is imported, each object can be selected separately. The objects describing slabs are first selected. A tool provided in the Arch workbench is used to transform these objects into parametric structural entities for which a role property is set (beam, column, slab, roof, foundation, etc.). Similarly, objects describing walls are selected and transformed into parametric wall entities. During the creation of wall entities, walls which are close from each other are joined automatically. Figure 9 shows structural and wall entities created in FreeCAD. One should note that the creation of entities is based on volumes and therefore walls described only by one surface disappear.  
Finally, structural and wall entities are grouped into a building entity and a file in IFC format is exported. The whole steps carried out in the FreeCAD software (import, creation of structural, wall and building entities and export) can be automated thanks to a script in Python. To check if the export in IFC format works properly, the generated file for the 1 st floor of the INSA building was imported into several viewers and several BIM software. The IFC file opened in Revit (Autodesk), a leading BIM software in the market, is presented Figure 10. At this stage, one can imagine to enrich semantically the created model by adding for example information about materials composing walls and slabs.
# GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
## 5 Conclusions  
An improved position estimation method in the position domain was proposed, and the GPS + BDS network real-time differential positioning using this improved method was evaluated. The results show that:  
1. The RMS errors and STD values of the position errors can be reduced by around 10%-40% using the improved method compared with the traditional algorithm in the range domain. The improved method is less affected by the satellite constellation.
2. The positioning accuracy and availability of GPS + BDS solution is better than those of GPS-only solution, owing to the increasing number of observed satellites. The RMS errors of GPS + BDS network real-time differential positioning using the improved method can reach up to 0.217 m, 0.159 m and 0.330 m in the N, E and U components, respectively, for the static user station, and 0.122 m, 0.133 m and 0.432 m for the dynamic user station.
3. The positioning accuracy variation does not only depend on whether the user is inside or outside the network, but also on the position relation between the user and network.  
It should be noted that the assumed precision of GPS and BDS code and carrier phase measurements may not be that realistic, and the stochastic model using the same elevation-dependent model for each satellite type may not be that appropriate. An investigation to obtain the proper weighting strategy for GPS and BDS satellites will be made.  
Conceptualization, J.C. and W.T.; formal analysis, J.C., R.Y., C.D., Q.L. and Y.L.; investigation, R.Y., X.Z., M.S. and Y.W.; methodology, J.C. and W.T.; software, J.C.; writing--original draft, J.C.; writing--review and editing, C.D. and W.T..  
This research was funded by the National Key Research and Development Program of China (Grant No. 2017 YFB0503702), National Natural Science Foundation of China (Grant Nos. 41874037, 41804028), \"the Fundamental Research Funds for the Central Universities\" (Grant No. 2042018 gf0001) and the Wuhan Science and Technology Project (Grant No. 2018010401011271).  
We are very grateful to Mengtang Hui at the North Information Control Institute Group Co., Ltd. for the dynamic data. We thank the anonymous reviewers for their constructive comments and suggestions.  
The authors declare no conflicts of interest.
## 4 Experiments  
In order to test our algorithms, we used several benchmarks that are available for use by researchers [19]. We selected the benchmarks of _S. aureus_ MW2 with short reads. The benchmarks that we used have the following number of fragments: 2,278,504, 730,201, 298,194, 89,718 and 22,447. From now on, we will refer to each one of these problems by its number of fragments. In order to have something to compare our results to, we ran the Edena assembler using the same data, Table 4. This comparison has some limitations due to the fact that Edena uses a series of heuristics in order to refine its solution, by eliminating _contigs_ that have no real meaning, while our results are those directly generated by our algorithms with no refinement. Refining the raw results from our algorithms might be part of our future research. \begin{table}
\begin{tabular}{c|c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Problem**} \\ \hline
**Fragments** & 22,448 & 89,718 & 298,194 & 730,201 & 2,278,504 \\
**Contigs Obtained** & 186 & 258 & 575 & 837 & 2046 \\
**Total Number of Bases** & 26,284 & 102,684 & 314,120 & 788,198 & 2,526,278 \\ \hline  & **Contigs Found** & 184 & 256 & 565 & 823 & 1920 \\  & **\%** & 98.9 & 99.2 & 98.3 & 98.3 & 93.8 \\  & **Bases Found** & 26,024 & 102,249 & 310,965 & 777,583 & 2,463,563 \\
**Contigs** & **\%** & 99 & 99.6 & 99 & 98.7 & 97.5 \\
**Found** & **N50** & 188 & 1247 & 2911 & 4132 & 5250 \\  & **Average Length** & 141.4 & 399.4 & 550.4 & 944.8 & 1283.10 \\  & **Minimum Length** & 52 & 52 & 52 & 52 & 52 \\  & **Maximum Length** & 880 & 7295 & 10,985 & 20,521 & 20,579 \\ \hline  & **Contigs not Found** & 2 & 2 & 10 & 14 & 126 \\  & **\%** & 1.1 & 0.8 & 1.7 & 1.7 & 6.2 \\  & **Bases not Found** & 260 & 435 & 3155 & 10,615 & 62,715 \\
**Contigs** & **\%** & 1 & 0.4 & 1 & 1.3 & 2.5 \\
**not Found** & **N50** & NA & NA & NA & NA & 5599 \\  & **Average Length** & 130 & 217.5 & 315.5 & 758.2 & 497.7 \\  & **Minimum Length** & 99 & 99 & 56 & 56 & 52 \\  & **Maximum Length** & 161 & 336 & 2521 & 5941 & 18,158 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Edena benchmark results. From each benchmark, we took only the fragment file in FASTA format, and from it we computed the overlaps among all fragments considering that reverse-complement fragments are also independent fragments (In the original benchmarks, there is a file of overlaps where reverse-complements are not considered as independent fragments). To compute overlaps, we used a _trie_ (prefix tree). The computed overlaps are all larger than 20 bases. From these overlaps other sets of test data were obtained: one discarding redundant overlaps because of transitivity, and the other one, computing the MST. In all of the cases, the minimum agreement in Algorithm 5 was 4. The _contigs_ obtained in each experiment were searched in the complete genome [20]. In Experiment 1, we used all of the overlaps larger than 20 bases and executed Algorithm 1 for the two objective functions. The results are presented in Tables 5-7.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 2 Related Work  
### Blind Image Deblurring  
Image deblurring can be divided into two categories: non-blind deblurring (a priori known blur kernel \(K\)) and blind deblurring (unknown \(K\)). Since the degradation representations of real-world blurred images are spatio-temporal variants [4; 27; 28], non-blind deblurring methods cannot accommodate blur changes due to object movement and scene depth. Therefore, blind deblurring is now more widely studied. Although the blur kernel is unknown, early blind deblurring works still assume that it is uniformly distributed throughout the whole image [2; 29]. However, real-world blurred images often have different blurring regions of an image composed of various blur kernels. Methods based on the a priori assumption of uniform blur kernels do not perform well in dynamic scenes due to camera shake and 3D blurring. To solve this problem, scholars have proposed many deep-learning-based methods for dynamic scene deblurring [8; 9; 19]. [PERSON] et al. [8] present a multi-scale CNN-based network to directly map the various source-blurred images to latent sharp images. [PERSON] et al. [9] proposed a scale-recurrent network(SRN-DeblurNet), whose input is a series of multi-scale blurred images. SRN-DeblurNet then learns blurring features in the images and outputs the corresponding sharp images through the encoder-decoder structure of residual blocks, residual skip connections, etc. The network proposed by [PERSON] et al. [19] also adopts an encoder-decoder structure to extract the blurred features. Unlike [PERSON] et al. [9], they added Parameter Selective Sharing for CNN parameters to the network in order to achieve better deblurring performance. However, the methods mentioned above do not sufficiently extract the degradation representations of blurred images, which leads to a decrease in deblurring performance in the face of more complex real blurred images.
## 2 Materials and Methods
### Methodology  
Based on the time-series SAR images of rice-growing season, after multi-looking processing, registration, polarization filtering, and geographic coding, the polarization post-scattering coefficient of typical ground such as rice, artificial building, water, and vegetation was extracted by sample point. After combining with the InSAR technology, the baseline estimation, interference generation, adaptive filtering, and coherence calculations, the VV and VH polarization coherence coefficients of the SAR images were obtained. The polarization coherence coefficient of typical samples such as rice, artificial building, water, and vegetation was extracted from sample data, and then the variation of the time-series coherence coefficient of typical ground objects was analyzed. Selecting the appropriate time-series SAR image polarization information and the coherence coefficient, the contribution of coherence coefficient to time-series SAR image was studied. How to select the appropriate polarization information and the coherence coefficient to extract the rice-planting area information was the key point and difficulty of this study.  
Based on the time-series SAR images and rice improvement identification results of coherence-coefficient SAR images, Sentinel-2 optical image and grain statistical data were verified data with the aim to verify the reliability of rice identification results of SAR images.  
The framework of the study was as follows (Figure 2).  
Figure 1: Location of the study area.
## 6 Effects of Roasting on Polyphenol Content
### Pistachios  
With regard to hazelnuts and almonds, the roasting process triggers the release of polyphenols that would have been otherwise bound and unmeasurable. In this instance, the total phenolic content generally amounts to 25 mg/100 g, while on single roasted almonds it is around 32.4 mg/100 g. Furthermore, the third roasting process increases the content to 42.4 mg/100 g [67].  
Moreover, [PERSON] et al. (2022) quantified both bound and free polyphenols and flavonoids in raw and roasted pistachios. They reported that bound polyphenols in raw pistachios were 85.1 mg GAE/100 g, while in roasted pistachios, they were 73.1 mg GAE/100 g. Free phenolics, on the other hand, were higher in raw pistachios (394.8 mg GAE/100 g)compared to in roasted pistachios (374.4 mg GAE/100 g). Regarding flavonoids, bound flavonoids were more abundant in raw pistachios (62.5 mg CE/100 g) than in roasted pistachios (42.7 mg CE/100 g), expressed in terms of the equivalent amount of catechin (CE). Free flavonoids were also higher in raw pistachios (115.8 mg CE/100 g) compared to roasted pistachios (101.4 mg CE/100 g), where penstisic acid was the most abundant phenolic acid detected in both raw and roasted pistachios. In raw pistachios, gentisic acid was followed by gallic acid and protocatechuic acid in terms of abundance. Similarly, in roasted pistachios, gentisic acid remained the dominant phenolic acid, followed by gallic acid and protocatechuic acid. The authors of this study have demonstrated that the roasting process can lead to positive alterations in the phytochemical composition of almonds, thereby facilitating the increased bioavailability of health-promoting compounds [81].  
In another study, [PERSON] et al. (2017) reported that the amount of polyphenols in raw pistachios was 670 mg GAE/100 g. Following roasting treatment at 110 \({}^{\circ}\)C for 16 min, the polyphenol content increased to 1050 mg GAE/100 g [80].
## 3 Materials and Methods
### Hardware System Design
#### 3.2.2 Shuttle Shifting Actuator  
In this study, a commercial actuator for shuttle transmission with a voltage of 12 V, rated torque of 294 Nm, rotational speed of 45 RPM, and current of 5 A was used. A shuttle shifting actuator was developed to shift the shuttle gear of a synchronesh-type and connect to the shuttle linkage. The shuttle shifting actuators were controlled by an input voltage (0-12 V), and potentiometers were installed in each actuator for position control by rotation of the motors. The position ranges of the shuttle shifting actuators were decided by rotational displacement of the motors. The ranges of the shuttle shifting actuator were divided into 20\({}^{\circ}\) intervals from 15\({}^{\circ}\) to 75\({}^{\circ}\) at reverse, neutral, and forward, as shown in Figure 5a. The potentiometer ranges of the shuttle shifting actuator, as described above, were determined experimentally. The potentiometer mounted inside the shuttle shifting actuator was calibrated using an external potentiometer (SP2800, Novotechnik, Germany). The potentiometer calibration result of the shuttle shifting actuator showed a coefficient of determination of 0.99, indicating that closed-loop control using the potentiometer of the shuttle shifting actuator is possible, as shown in Figure 5b.
## 4 The Proposed Intelligent Surface Roughness Estimation Method  
As a type of feed-forward artificial neural network, CNN possesses good hierarchical feature representation ability from lower levels to higher levels [39]. In this paper, a novel intelligent surface roughness estimation method based on 2D-DTCWT and CNN is proposed for surface roughness estimation problems. The flow chart of the proposed method is presented in Figure 10. Relevant details are also described below.  
Step 1. Place the camera on a machined workpiece surfaces to capture the surface images. The surface roughness is estimated by the measuring instrument.  
Step 2. Correct the texture skew of the original image with the proposed skew correction algorithm in Section 2.  
Step 3. Decompose the adjusted images using 2D-DTCWT with a depth of \(n\) to retrieve the useful surface textures. The 2D-DTCWT decomposition depth is set to 3.  
Step 4. Divide the processed images into two groups, namely the training dataset and test dataset. The processed images are classified into 10 classes according to the surface roughness measured by the measuring instrument. In this model, each example is a combination of an input object (an image in this research) and a desired output value (designated roughness class). The model is trained with such iterations. The test dataset is utilized to validate the trained ResNet. In this paper, 14 layers are employed for the intelligent surface roughness estimation.