esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Wind Directionality Effects of Extreme Wind Loads  
A directionality factor is often used in building codes (ASCE7-2010) to consider the wind load reduction effect from the worst-case estimation. However, the directionality factor is generally associated with many other factors, such as characteristics of directional wind speed and aerodynamics. To consider the effects of wind direction, the wind direction factor is introduced for calculating the design wind load. Assuming that the wind speeds in all directions are completely correlated, this study adopts the sector-by-sector method to conduct wind directionality effects studies based on the wind speed-wind direction data of a typical region [18]. The directionality factor is given as  
\[K_{\text{d}}=P_{R}/P_{N,R} \tag{10}\]  
\[P_{R}=0.5\rho V_{R}^{2}(C_{p}(\alpha))_{\text{MAX}} \tag{11}\]  
\[P_{N,R}=0.5\rho[V_{R}^{2}(\alpha)*C_{p}(\alpha)]_{\text{MAX}} \tag{12}\]  
where \(P_{R}\) is the wind-induced pressure for return period \(R\) considering the wind direction effect, \(P_{N,R}\) is the wind-induced pressure for return period \(R\) under the worst case among all the wind directions, \(\rho\) is the air density, \(C_{p}(\alpha)\) is the extreme pressure coefficient in \(\alpha\) direction, \(V_{R}\) is a design wind speed of the worst case for period \(R\), and \(V_{R}(\alpha)\) is the design wind speed for period \(R\) in \(\alpha\) direction.  
Figure 14: Fitting result of the probability distribution of K\({}_{\text{a}}\) value in different areas: (a) 5 m\({}^{2}\), (b) 10 m\({}^{2}\), (c) 25 m\({}^{2}\).  
Figure 13: Reduction factors of block extremums of different areas under different wind directions: (a) 5 m\({}^{2}\), (b) 10 m\({}^{2}\), (c) 25 m\({}^{2}\).  
The wind directionality factor of the 50-year return period for 16 directions is estimated in the present study. The wind speed and the wind direction data were obtained from Shanghai Longhua Meteorological Station between 1959 and 1990. The parameters of 16 wind direction extreme value distributions conforming to the Gumbel distribution are given in Figure 15. The extreme wind speed with a return period of 50 years is assessed, and then the directionality factors of all measurement points are calculated according to Equations (10)-(12).  
Figure 15: Gumbel distribution parameter estimation results of wind speed and direction joint distribution. (a)\(\alpha\) of Gumbel distribution. (b) \(\mu\) of Gumbel distribution.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 3 Proposed Multi-Color Channels Based GSR Model
### Construction of the Proposed Model  
#### 3.1.1 Construction of Image Groups  
For an input degraded image \(y\), the image group \(Y_{i}^{Ch},Ch\in\{R,\ G,\ B\}\), is constructed based on the multi-color channel image, where \(R\), \(G\) and \(B\) denote the corresponding image channels. The construction of each channel image group \(Y_{i}^{Ch}\) is the same as that of \(Y_{i}\) in Section 2.2. Given a RGB image, in each channel, \(m\) similar patches of the repairing patch with the size of \(\sqrt{b}\times\sqrt{b}\) are found in the training window with the size of \(W\times W\). \(Y_{i}^{R},Y_{i}^{G}\), and \(Y_{i}^{B}\) denote the image groups corresponding to the \(R\), \(G\) and \(B\) channels, respectively. The three image groups form the total image group \(Y_{i}^{C}\) as Formula (9).  
\[Y_{i}^{C}=\left[\begin{array}{cccc}Y_{i,1}^{R}&Y_{i,2}^{R}&\ldots&Y_{i,m}^{R} \\ Y_{i,1}^{G}&Y_{i,2}^{G}&\ldots&Y_{i,m}^{G}\\ Y_{i,1}^{B}&Y_{i,2}^{B}&\ldots&Y_{i,m}^{B}\\ \end{array}\right]\in R^{3b\times m} \tag{9}\]  
\[Y_{i}=\left[Y_{i,1}^{Y}\ Y_{i,2}^{Y}\ \ldots\ Y_{i,m}^{Y}\right]\in R^{b \times m} \tag{10}\]  
The above Formula (10) denotes the image group constructed using the Y channel in a traditional GSR model. The traditional GSR model and its improved models transform the RGB space into YCbCr space and extract the Y component for processing. The advantage of traditional GSR model is that human vision is sensitive to the change of the Y component in YCbCr space, and the Y component requires fewer storage units than the three color channels in RGB space, which is conducive to transmission and processing. However, the Y component cannot be loyal to each color channel in image processing. In this paper, as shown in Equation (9), stacking the image groups of three different color channels R, G and B in a matrix make it possible to tackle the three channels simultaneously. The total image group \(Y_{i}^{C}\) standing for the three color channels can be directly decomposed by singular value decomposition to obtain an adaptive dictionary and the sparse representation model. In the proposed model, the total image group is processed at the same time to avoid the loss of information caused by the transformation from RGB space to YCbCr space. Moreover, the rich information between different color channels can make up for each other [33]. On the other hand, there is no need to set and adjust the parameters of each color channel respectively when processing three color channels simultaneously, compared with separately processing the image group of each color channel to fuse different results to form the final combination results. Therefore, the proposed construction of image groups is more accurate that of the traditional GSR model. The advantages of the proposed multi-color-channels-based GSR model are low computational cost, low time cost and high efficiency.
## 2 Materials and Methods
### Alamar Blue Cytotoxic Assay  
After 24 h of incubation, the test solutions or culture media from each well were removed and then rinsed with 200 \(\mu\)L of phosphate-buffered solution. This step was taken to prevent any potential interaction between test solutions and the Alamar Blue reagent. Then, 100 \(\mu\)L of fresh complete culture media was added to each well. Blank wells were filled with culture media without cells. Then, 10 \(\mu\)L of Alamar Blue reagent (Thermo Fisher Scientific, Waltham, MA, USA) was added to the wells and left incubated at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) for 4 h. Later, the Alamar Blue reagent reduction was measured by measuring the fluorescence (reduced fluorescence unit, RFU) at an emission wavelength of 590 nm and excitation wavelength of 560 nm using the microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). The test was conducted in triplicate and repeated at three independent times, \(n=9\). The percentage of reduction was calculated to determine the gain adjustment of fluorescence for each well against the well with fully reduced Alamar Blue reagent, in line with the manufacturer's protocol [39] and [PERSON] et al. [40]. The percentage of Alamar Blue reagent reduction serves as a quantitative indicator of metabolic activity and reducing capacity of live cells. It measures the ability of viable cells to convert the non-fluorescent oxidised resazurin into the fluorescent reduced resorufin [41]. A higher percentage of reduction indicates greater cell viability [42].  
\[\%\] reduction of alamar blue reagent \[=\frac{\text{experimental RFU value}-\text{blank RFU value}}{100\%\text{ reduced positive control RFU value}-\text{blank RFU value}}\times 100 \tag{1}\]  
Additionally, the relative cell viability expressed as a percentage of untreated control was calculated in accordance with ISO 10993-5 [37].  
\[\text{Relative cell viability}=\frac{\text{experimental RFU value}-\text{blank RFU value}}{\text{untreated negative control RFU value}-\text{blank RFU value}}\times 100 \tag{2}\]
# Upgrades of a Small Electrostatic Dust Accelerator at the University of Stuttgart
## 4 Discussion  
We upgraded our small dust accelerator with one newly developed dust source, one differential dust detector and one linac stage. The PSU system was also tested in the new beam line. Various conductive metal and coated powders were tested. The accelerator has already been used for a wide variety of experiments to investigate the interaction of the high-speed dust particles with a solid surface and thin foil, such as impact cratering/penetration, impact fragmentation, impact ionization, impact flash, etc. Several calibration tests of the in situ dust detector have also been performed with this small dust accelerator. A new PSU system with differential detectors is under development. Improvements in the acceleration of a wider variety of dust materials and detection and selection of dust particles has led to an increasing number of applications.  
Figure 11: Parameters of accelerated copper particles with a voltage of 19 kV. (**a**) Particle speed and mass distribution; (**b**) Particle charge-to-mass ratio varies with mass; (**c**) Particle size distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Supplier** & \(U_{cc}\)**, **kV** & \
\begin{tabular}{c} **Event** \\ **Number** \\ \end{tabular} &
\begin{tabular}{c} **Grain Size,** \\ **\(\mu\)m** \\ \end{tabular} & **Velocity, m/s** \\ \hline Iron & ThermoFisher & 7–20 & 16,218 & 0.02–10 & 11–7140 \\ Copper & ThermoFisher & 9–12 & 20,081 & 0.02–10 & 12–6667 \\ Carbon & ThermoFisher & 3–12 & 2570 & 0.4–12 & 10–1530 \\ SiO\({}_{2}\)1 & [PERSON] [PERSON] 2 & 3–9 & 810 & 0.4–0.9 & 10–1470 \\ Peridot 3 & Dr. [PERSON] 2 & 15 & 761 & 0.7–3 & 17–939 \\ \hline \hline \end{tabular}  
* Polypyrrole-coated. 2 Personal communication. 3 Lead-coated.  
\end{table}
Table 4: Parameters of accelerated dust particles at the small dust accelerator.  
Figure 12: Needle electrode with adhered dust particles.  
**Author Contributions:** Conceptualization, Y.L.; Methodology, Y.L., M.B., S.K., H.S., J.S., C.M., M.S. and A.M.; writing--original draft preparation, Y.L., M.B. and S.K.; writing--review and editing, Y.L., M.B., S.K. and R.S.; supervision, Y.L. and R.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by Deutsche Forschungsgemeinschaft (DFG) grant BE 2256/31-1 and grant SR 77/6-1.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this paper are available on request from the corresponding author.  
**Acknowledgments:** The authors wish to thank [PERSON] and [PERSON] for their technical support in hardware development and accelerator operation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Three-Dimensional, Time-Dependent Analysis of High- and Low-\(Q\) Free-Electron Laser Oscillators
## 2 Numerical Formulation
### The MINERVA Simulation Code
Note that SDE (Equations (9)-(12)) recovers vacuum diffraction when no electron beam is present \([X_{h}^{(j)}(z,t)=Y_{h}^{(j)}(z,t)\equiv 0]\). The source terms in Equation (5) depend on the evolution of the electron coordinates and velocities that are given by the Newton-Lorentz equations  
\[\frac{dx}{dz}=\frac{v_{x}}{v_{z}}, \tag{13}\]  
\[\frac{dy}{dz}=\frac{v_{y}}{v_{z}}, \tag{14}\]  
\[\frac{d\mathbf{p}}{dz}=-\frac{e}{v_{z}}\Big{[}\delta\mathbf{E}+\frac{\mathbf{ v}}{c}\times(\mathbf{B}+\delta\mathbf{B})\Big{]}, \tag{15}\]  
and the evolution of the ponderomotive phase \(\psi\) given by  
\[\frac{d\psi}{dz}=k+k_{u}-\frac{\omega}{v_{z}}. \tag{16}\]  
Using the Coulomb gauge, the optical fields \(\delta\mathbf{E}=-\frac{1}{c}\frac{\partial\xi\mathbf{A}}{\partial t}\) and \(\delta\mathbf{B}=\
abla\times\delta\mathbf{A}\) are derived from the optical vector potential given by Equation (1), while \(\mathbf{B}\) is the static magnetic field produced by the magnetic elements along the electron transport line. The model presented here ignores space-charge effects, however, this can be easily incorporated [1]. The dynamical equations for the particles and fields are integrated simultaneously using a 4 th order Runge-Kutta algorithm. Hence, the number of equations in the simulation is \(N_{\text{equations}}=N_{\text{slices}}[6N_{\text{particles}}+4(N_{\text{modes}}+N_{ \text{harmonics}})]\), where \(N_{\text{slices}}\) is the number of slices in the simulation, and \(N_{\text{particles}}\) is the number of particles in each slice, \(N_{\text{modes}}\) is the total number of modes in the fundamental and all the harmonics, and \(N_{\text{harmonics}}\) is the number of harmonics. The Runge-Kutta algorithm allows the step size to change so optimized step sizes can be used in each magnetic element or in the drift spaces while this imposes no limitation on the placement of components along the electron beam path. The formulation self-consistently tracks the generation of the optical field with arbitrary polarizations depending on the undulator configuration. The polarization state of the output light, therefore, can be determined by calculation of the Stokes parameters [194]. Measurements of the polarization in FELs have been characterized by the Stokes parameters [195; 196]. Magnetic field elements, such as undulators, quadrupoles and dipoles, can be placed in arbitrary sequences to specify a variety of different transport lines, and the gap lengths between different undulators can be varied as well. Field configurations can be set up for single or multiple undulator segments and can contain quadrupoles placed between the segments or be superimposed on the lattice to create a FODO lattice. Magnetic elements can also be dipole chicanes to model optical klystron, high-gain harmonic generation (HGHG) or phase shifters. See Section 2.2 for analytical models for the various static magnetic field elements.
# Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning
###### Abstract  
Within the Pattern Recognition field, two representations are generally considered for encoding the data: _statistical_ codifications, which describe elements as feature vectors, and _structural_ representations, which encode elements as high-level symbolic data structures such as strings, trees or graphs. While the vast majority of classifiers are capable of addressing statistical spaces, only some particular methods are suitable for structural representations. The \(k\)NN classifier constitutes one of the scarce examples of algorithms capable of tackling both statistical and structural spaces. This method is based on the computation of the dissimilarity between all the samples of the set, which is the main reason for its high versatility, but in turn, for its low efficiency as well. Prototype Generation is one of the possibilities for palliating this issue. These mechanisms generate a reduced version of the initial dataset by performing data transformation and aggregation processes on the initial collection. Nevertheless, these generation processes are quite dependent on the data representation considered, being not generally well defined for structural data. In this work we present the adaptation of the generation-based reduction algorithm Reduction through Homogeneous Clusters to the case of string data. This algorithm performs the reduction by partitioning the space into class-homogeneous clusters for then generating a representative prototype as the median value of each group. Thus, the main issue to tackle is the retrieval of the median element of a set of strings. Our comprehensive experimentation comparatively assesses the performance of this algorithm in both the statistical and the string-based spaces. Results prove the relevance of our approach by showing a competitive compromise between classification rate and data reduction.  
Keywords:string space; data reduction; \(k\)-Nearest neighbor; prototype generation +
Footnote †: journal: Applied Sciences
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 3 Proposed Methodology
### Vehicular Noise  
The acquisition of data originating from vehicular noise, utilizing the aforementioned geophone system in Section 3.2, constituted a crucial phase of our experiments. The key requirement was to identify a location to place the sensor on a major road with a gap close to the road for the positioning of the sensor. Additionally, in order to be able to monitor the collection process, the location had to be opportune for human operators tasked with recording the passing vehicles so that we could confirm the ground truth and align it with the signals captured by the geophone. To fulfill all these requirements, we selected a frequently accessed road in close proximity to our laboratory in the Garitsa area in Corfu, Greece. For all the above constraints, we collected the signals of passing cars via the geophone of the sensor, along with the audio recording, in order to assist in the labeling phase of ground truth later on. Using these recordings, we created an annotated dataset containing vehicular noise. In the post-collection process, to create the final dataset for vehicular noise, we cleaned and labeled the data by selecting the most representative axes of the geophone data containing the records from the movement of the ground. For the labeling process, the timestamps were labeled manually using the synchronous audio mentioned previously, collected concurrently with the geophone data, each time a vehicle passed. In order to further support the reproducibility of our work, the data of this dataset are available online ([[https://drive.google.com/drive/folders/1_H72](https://drive.google.com/drive/folders/1_H72) gqp2 ObBizB_YHRI0]([https://drive.google.com/drive/folders/1_H72](https://drive.google.com/drive/folders/1_H72) gqp2 ObBizB_YHRI0) u53 yTSHRiLqc?usp=drive_link, accessed on 13 September 2023).
## 4 Discussion
### Practical Implications  
Our study presents empirical findings on procedural information intake behaviors in the authentic context of maintenance tasks. The research involved using the procedure in its natural paper format, without any imposed restrictions or specific usage instructions. The maintenance task was extensive, lasting over an hour and requiring the execution of multiple instructions from a document comprising numerous pages. Understanding how aircraft maintenance technicians use aircraft maintenance manuals has practical implications for procedural design and AMT training. The information intake within AMMs is achieved through multiple short consultations, averaging a duration of 10.8 s. Good readerscan process information at a rate of 200 to 400 words per minute [86; 87]. This indicates that the amount of information absorbed during each consultation is relatively small. In document design, it is important to optimize the physical format to facilitate the efficient location of relevant information [54; 88]. Our study highlights the potential challenges faced by novice individuals when initiating the maintenance task; training programs can be designed to emphasize the initial interaction with the global procedural document at the start of a task. Consequently, workers could learn to extract the important cues for planning their entire task. Our study shows that mobile eye tracking is suitable for field studies in the context of aircraft maintenance, even over long periods of measurement with procedural documents in paper format.
## 3 Results and Discussion
### Panel Performance  
Based on the panel test, for two of eleven attributes (bitter and acid), the interaction between sample and panelist was significant (\(p<0.05\)), indicating that for those attributes the panelists differed in use of the scale. The factor \"panelist\" had a significant effect (\(p<0.05\)) for all the attributes. Furthermore, for all attributes, there were no significant effects of the replicate (\(p>0.05\)). Overall, 7 out of 12 attributes (\(p<0.05\)) were found to discriminate the samples (Figure 3). Several factors could affect the color of a commercial grated bottarga sample: the quality of the raw material, the procedures (salting, drying, grinding, and storage), and the conditions (time, temperature, and light exposure) of production.  
The BEC samples exhibited significantly higher scores than BWC, indicating a more homogeneous appearance of slice color uniformity. This could be attributed to differences in the salting or drying methods, which may affect the distribution of pigments in the final product.  
No significant differences were observed in bottarga color uniformity, suggesting a homogeneity in the raw material quality or preliminary processing phases, such as ovary selection and handling. The texture attributes (greasiness, adhesiveness, and friability) did not show significant differences between the two samples. This could reflect similar drying techniques and the fat content levels, which can influence texture-related perceptions. The global odor and the sea odor were higher for the west coast bottarga, suggesting a more pronounced aromatic profile. This could be due to variations in the diet of the  
Figure 3: Radar/spider plot of the mean rating scores for the asterisks denote samples that were significantly different (\({}^{*}p<0.05\); \({}^{**}p<0.01\)). BEC, bottarga east coast; BWC, bottarga west coast.  
fish or to environmental conditions that affect the raw material, such as water salinity or water temperature.  
The west coast sample (BWC) also produced a significantly higher score for global flavor and sea flavor, probably due to the natural biochemical composition of the roe due to differences in mullet environmental conditions; however, differences related to specific handling practices during processing should be considered.  
The acid and salty attributes showed a significantly higher score for the BWC bottarga compared to BEC, reflecting a sharper and more balanced taste. This could indicate differences in salting methods during drying, which can have a greater impact on flavor than on other sensory traits. No differences in bitterness were found between the samples, suggesting that this attribute is not affected by origin or processing procedures. Furthermore, overall consumer acceptance (Figure 4) also showed no significant differences between BEC and BWC.