esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Classification of Interference
Figure 13: Comparison of the classification performance for different numbers of satellites (\(N_{S}\)) with CNN: (**a**) precision, (**b**) recall. To complement the previous results for accuracy, precision, and recall, we provide in the following Figures 15 and 16 and related sub-figures the confusion matrices for different values of \(B_{D}\), \(L_{I}\), and \(N_{S}\). Predicted values are on the y-axis and True values are on the x-axis. The figures were generated using the CNN with \(L_{I}=L\), which is the most challenging for classification. Figure 16: _Cont._  
Figure 14: Comparison of the classification accuracy for different numbers of satellites with CNN and \(L_{I}\): (**a**) \(N_{S}=1\), (**b**) \(N_{S}=8\). Figure 15: Confusion matrices with different number of satellites \(N_{S}\) (range from 1 to 2) and bit-depth \(B_{D}\) with low level of interference: (**a**) \(N_{S}=1\), \(B_{D}=2\), (**b**) \(N_{S}=1\), \(B_{D}=16\), (**c**) \(N_{S}=2\), \(B_{D}=2\), (**d**) \(N_{S}=2\), \(B_{D}=16\). The figures show that the Wide and Gaussian types of interference are more difficult to classify in comparison to the Narrow type of interference. As expected, the number of FPs and FNs is lower in percentage (values outside the diagonal) when more satellites or a higher value of \(B_{D}\) is used.
## 5 Conclusions  
This study has examined the validity of using the WBB to measure kinetic parameters while walking, which is a more dynamic motion than the sit-to-stand and static standing scenarios focused on in previous studies. In comparison to the laboratory-grade force plates, the accuracy of the stance duration, the vGRF and the COP trajectory while walking as measured by the WBB were evaluated. From the results, although it did not have satisfactory accuracy to be considered a replacement of laboratory-grade devices, the WBB may be used to take measurements in clinical settings for which lower accuracy is acceptable. In particular, the vGRF, for which the WBB showed a high accuracy compared with the force plate, is equivalent to the commercial instrumented insoles used as an alternative to the laboratory-grade devices.  
Further investigation is required to test individual variability in the measured force among multiple devices while walking, because this has not been considered in dynamic motions. In addition, future work should examine the static and dynamic calibration of the WBB to obtain a more accurate COP trajectory in dynamic motions, as determined from previous studies [11]. When clinicians use the WBB for gait analysis, some specifications of the WBB have to be considered, such as contact positions of the foot, because the WBB has a significant increase in COP error from the center to the corner locations [10]. Moreover, the WBB has a low height (\(53.2\) mm), and the device needs to be set to its usable surface at the same level as the walking floor in order to prevent stumbling in clinical settings.  
**Acknowledgments:** This study was supported by JSPS KAKENHI, Grant No. 16H04290, and JKA and its promotion funds from KEIRIN RACE (28-143).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the proposed method and the verification experiments; [PERSON] performed the experiments and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Numerical Simulation of the Hydrodynamic Performance and Self-Propulsion of a UUV near the Seabed
## 3 Numerical Methods
### Computational Domain and Boundary Conditions  
The computational domain contains two parts: the stationary domain and the rotational domain. The cross section of the stationary domain is a square with a length and width of 20 \(D_{A}\), which is the diameter of the maximum cross section of the UUV. The length of the flow field domain is 10 \(L_{A}\), which is the length of UUV to ensure the full development of both the upstream incoming flow and wake flow, and the nose is 4 \(L_{A}\) from the inlet boundary. The rotating domain is a cylinder with a diameter of 200 mm and a length of 60 mm. The detailed sizes of the domain and the boundary condition settings are shown in Figure 2. Information about the flow field is transferred between the stationary and rotating domains through an interface. By reference to the findings of other researchers, the size of this flow field domain satisfies the requirements of the calculation. Additionally, the results of the validation cases that adopt the same strategy show that the size of the computational domain is reasonable.  
Inlet: Uniform incoming flow; \(\overset{\rightarrow}{u}=\overset{\rightarrow}{u}_{c}\);  
Outlet: Pressure outlet; \(p=0\);  
Non-slip wall surfaces: For the hull, fin and propellers and subsea walls. \(\overset{\rightarrow}{u}=0\);  
Slip wall surfaces: For the top of the flow field. \(\frac{\overset{\rightarrow}{\partial x}}{\partial x_{i}}=0\);  
Symmetric boundary: For the side of the flow fields. \(u_{n}=\overset{\rightarrow}{u}_{c}\cdot\overset{\rightarrow}{n}=0\).
## 1 Introduction
### Assessment of Legibility  
A majority of the studies investigating the appropriate character and background color combinations have relied on subjective responses for their interpretations. These responses were gathered using diverse methods such as questionnaires, rating scales, or by measuring performance and errors in target identification [27]. Our study tested the impact of different color combinations--achromatic (black on gray), achromatic-chromatic (black on green), chromatic (purple on green)--as well as the effect of media (digital and printed). Eye movement activity in the digital medium was recorded using eye-tracking technology.  
The methodology of eye tracking has been gaining popularity as a method for evaluating visual attention processes by tracking eye movements during ad exposure, offering useful insights into the cognitive mechanisms driving consumer behavior [28]. To better understand how human visual attention works in a digital setting, it is important to consider that the perception of promotional material refers to the ad's ability to attract attention and maintain it [29]. An objective measure of both attention stages is the viewers' eye motion patterns.  
The importance of eye fixations in advertising and brand memory has been confirmed through previous studies, yet little attention has been given to the long-term impact of attention on people's cognition [28]. Research by [PERSON] et al. focused on how elements of print ads (brand, image, and text) capture and transfer consumer attention, concluding, among other findings, that expanding the text's surface area draws more attention to this element than it simultaneously diminishes focus on the brand and image [30]. Enhancing visual attention by emphasizing ad elements improves brand perception and recall. Our research aims to contribute to better application of textual elements in the design of promotional materials.  
Although researchers most commonly use eye tracking to measure readability, they occasionally employ comprehension testing as well. This is particularly true for studies similar to ours, which compare the impact of background or medium [4, 31, 32]. According to [PERSON] et al. [4], the results of reading speed demonstrated the significant impact of paper characteristics. In the context of text comprehension across different media (screen versus paper), [PERSON]'s research [31] was conducted as a meta-analytical study, focusing on the collection and analysis of texts used in the included studies, which primarily consisted of narrative texts. Reading comprehension was assessed using various types of tests, including open-ended questions, multiple choice questions, and retelling (content reproduction). In the study conducted by [PERSON] and [PERSON] [32], the participants read four texts (two digital and two printed) in a balanced order. After reading each text, they answered comprehension questions in the same medium in which they had read the text. Therefore, our research also implemented a comprehension test, along with the eye tracking, to investigate the influence of different color combinations on various media types.
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 5 Conclusions  
Our research presented the spatial-temporal distribution of land subsidence in a Tianjin study area based on 20 Sentinel-1A SLC SAR scenes collected from March 2017 to March 2019 using two MT-InSAR methods. Both PS and SBAS techniques successfully estimated the extent of land subsidence and recognized a similar subsidence pattern as a result of excessive extraction of groundwater in Tianjin. Both of the two methods suffer from low coherence, being the PS technique more sensitive. The subsidence value obtained by PS is generally greater than the SBAS value, and the magnitude of their velocity difference seems to depend on the greater capability of the PS technique to discriminate between ground objects with different velocities. The study area exhibited prominent uneven subsidence characteristics in the past two years. The subsidence velocity in the suburbs is much larger than that in the urban areas, and in particular, high subsidence areas are mainly at the junction of the Wuqing, Xiqing and Beichen districts with a subsidence velocity of over \(-50\) mm/year. It is necessary for the local government and residents to act together to take effective measures to prevent increased subsidence in Tianjin's suburbs.  
Data curation, D.L.; formal analysis, D.L.; funding acquisition, X.H.; investigation, D.L., Y.S., Y.Z. and C.W.; methodology, D.L.; software, D.L., Y.S. and C.W.; supervision, X.H.; visualization, Y.Z.; writing--original draft, D.L.; writing--review and editing, X.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19060205), China Postdoctoral Science Foundation (Grant No. 2019M652487), and Key Laboratory of Coastal Environmental Processes and Ecological Remediation, YICCAS (Grant No. 2018 KFJJ05).  
The authors would like to thank the European Space Agency (ESA) for providing the Sentinel-1A data and Precise Orbit Determination (POD) ephemerides. NASA holds the ownership of SRTM3 version-4 digital elevation model (DEM).  
The authors declare no conflict of interest.
# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
## 3 Methodology  
This research aims to design and develop an online clustering algorithm for handling evolving data streams. To achieve this, this paper develops an online clustering algorithm for handling evolving data streams using a tempo-spatial hyper cube. The developed methodology is presented in Figure 1. As it is shown, it starts with the literature survey followed by the research gap. The research gap focuses on the evolving aspect of the data. The handling of evolving will be conducted by designing an online clustering algorithm for handling evolving data streams using tempo-spatial hyper cube. Afterward, the baseline approach that is BOCEDS is implemented. Integrating the research objective with BOCEDS yields an algorithm called: BOCEDS-TSHC. The evaluation of the performance is based on comparing the developed BOCEDS-TSHC algorithm using standard evaluation metrics and benchmarking datasets. The benchmarks algorithms are BOCEDS [15], CEDAS [14] and MuDi-Stream [16]. The datasets used for the evaluation are DNA, Spiral, Landsat, KDD Cup'99, and Traffic Datasets. A visualization of some of the used datasets is presented in Figure 2, which illustrates the evolving behavior of the datasets. The performance of the developed BOCEDS-TSHC algorithm is evaluated using two types of evaluation namely, quality and efficiency evaluation.  
Figure 1: Flowchart of the Developed Methodology.
# A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3 PDP)
## 1 Introduction
### Problem Formulation  
The 3 PDP is formulated as follows: given an initial point \(\mathbf{P}_{1}=(x_{i},y_{i})\) with orientation \(\theta_{i}\), a middle point \(\mathbf{P}_{\mathbf{m}}=(x_{m},y_{m})\), and a final point \(\mathbf{P}_{\mathbf{f}}=(x_{f},y_{f})\) with orientation \(\theta_{f}\), find the shortest concatenation of two Dubins paths that begin in \(\mathbf{P}_{\mathbf{i}}\), pass through \(\mathbf{P}_{\mathbf{m}}\), and reach \(\mathbf{P}_{\mathbf{f}}\) while respecting the constraints of having the maximum absolute curvature not exceeding \(\kappa_{\text{max}}\); see Figure 1. The problem is clearly one-dimensional, with the angle \(\theta_{m}\) at Despite being the only unknown of the problem. Once \(\theta_{m}\) is determined, the solution becomes trivial, since it is just twice the application of the standard Dubins problem.  
Despite being formally simple, the 3 PDP is quite involved: the function of the total length to be minimized is, indeed, not continuous and thus non-smooth. Therefore, classic optimization methods cannot be applied, and different methods are required [19; 21].
# Hybrid Precoder Using Stiefel Manifold Optimization for Mm-Wave Massive MIMO System
###### Abstract
The Stiefel manifold optimization algorithm makes use of the Riemannian gradient method to solve the optimization problem. The algorithm complexity is much lower than that of the linear optimization problem, which is measured with the calculation of gradient, total energy, and projection on the Stiefel manifold. It is also easy to parallelize. In this paper, a fast optimization algorithm based on the Stiefel manifold is proposed to increase the spectral efficiency. Manifold optimization has superior performance in terms of spectral efficiency using a nested-loop architecture. The proposed technique increases the convergence speed by decreasing the nested loop between \(F_{BB}\) and \(F_{RF}\). The proposed technique also increases spectral efficiency when compared with PE-AltMin. The remainder of this paper is organized as follows: The channel and the system model for the hybrid precoding Mm-Wave system are presented in Section 2. Section 3 shows the proposed Stiefel manifold-based optimization algorithm for the hybrid precoding problem and the computational complexity in detail. Numerous simulation results are shown in Section 4; they reveal the performance of the proposed hybrid precoding algorithm. At last, Section 5 consists of the conclusion of this paper. The following symbolizations are used in this paper: (A) \(i,j\) is a matrix with i rows and \(j\) columns; bold, upper-case 'A' is a vector; upper-case 'A' is a scalar; \(\mathcal{A}\) is a set; \(|\)A\(|\) is the determinant of (A) \(i\), \(j\); \(\mid\) A \(\mid\) I \(\mid\) F denotes the Frobenius norm of A; (A) \(i\), \(j\), \(T\), (A) \(i\), \(j\) *, and (A) \(i\), \(j\)-1 represent the transpose of (A) \(i\), \(j\), the conjugate transpose of (A) \(i\), \(j\), and the inverse of (A) \(i\), \(j\), respectively.
# Nearshore Benthic Mapping in the Great Lakes:
## 2 Materials and Methods
### Satellite Data Pre-Processing  
The European Space Agency's Sentinel-2B multispectral imagery was acquired over the study area on 10 July 2018 (S2B_MSIL1C_20180710T163839_N0206_R126_T16 TDN). This scene was selected as it was the closest in temporal proximity to the airborne lidar and hyperspectral imagery data collection acquisition. The Level 1C processed product, top of atmosphere (TOA) reflectance, was downloaded from the Copernicus Open Access Hub [40] and processed using Version 7.0.0 of Sentinel Application Platform (SNAP) software [41]. Prior to utilizing the imagery within the Sen2 Coral toolbox, the imagery was processed to the 2A product level, bottom of atmosphere (BOA) reflectance with terrain and cirrus cloud corrections. While atmospheric correction procedures are an important aspect of pre-processing and have direct bearing on reflectance products, it was not our intent to evaluate their influence on benthic mapping, as previously explored in [PERSON] et al. [22] and [PERSON] et al. [42]. In contrast, our aim was to examine the performance of readily available and open data products and software that can be used in basin-wide benthic mapping strategies. As a result, the Sen2 Cor280 algorithm was used within the SNAP software to develop BOA reflectance and applied to the imagery. Upon completion, all bands were resampled to 10 m spatial resolution and subset to the study area.  
The Sen2 Coral toolbox v1.1 [43] within SNAP was used to generate both a Depth Invariant Index (DII) and Empirical Bathymetry, or Satellite-Derived Bathymetry (SDB), grids for use in the classification. The methodology provided in the Copernicus Research and User Support for Sentinel Core Products Sen2 Coral training [43] was applied to help with deglinting and masking of land, clouds, and surface waves present in the BOA reflectance imagery. The DII was derived by using the [PERSON] [44] and [PERSON] et al. [45] methods in which reflectance attenuation is approximately an inverse exponential with water depth. The SDB was derived by using the [PERSON] et al. [13] method in which water reflectance attenuation varies between different wavelengths captured in the satellite bands and determining the ratio between the bands with depth to develop relative bathymetry. Bathymetric points were selected from the airborne lidar point cloud that fell within the study area in which 935 points were used to train and develop the SDB product. Afterwards, an additional 2000 randomly sampled points were used to validate the resulting SDB against the airborne bathymetric lidar point cloud. All satellite products developed for classification input are shown in Figure 3, panels a-c.
## 3 Methodology
### Microfluidic Chip Fabrication  
Microchannel design was transferred to the glass chrome mask by direct lithography. A 2.5-inch AZ-coated glass chrome mask AZP1350 (ULCOAT, Saitama, Japan) and a positive photoresist were exposed with the channel design in a mask aligner with ultraviolet (UV) power of 12 mW (\(\upmu\)PGUV-N, Heidelberg Instruments, Heidelberg, Germany). The design-exposed masks were developed by dipping the masks into the chrome etchant for 1 min and rinsing them with deionized water (dH\({}_{2}\)O) to wash away the chrome etchant, followed by dipping into 70% ethanol for 1 min to remove the AZ layer. The chrome masks were further cleaned in 70% ethanol for 2 min in an ultrasonic cleaning machine. Six-inch-diameter silicon wafers were cleaned with dH\({}_{2}\)O and dried. The wafers (SUMCO Corp., Tokyo, Japan) were used as the substrate for mold development. SU8-3005 was spin coated onto the wafers at 5000 rpm for 10 s to get a thickness of 15 \(\upmu\)m followed a reduced spin speed at 1000 rpm for 30 s. The SU8-coated silicon wafers were soft baked at 95 \({}^{\circ}\)C for 10 min. The wafers were then exposed to UV light (\(\upmu\)PGUV-N, Heidelberg Instruments, Heidelberg, Germany) using a mask aligner for 8 s at 200 mJ/cm, post-baked at 95 \({}^{\circ}\)C for 6 min, developed using SU8 developer for 6 min, dipped into fresh SU8 developer, and dried using an air blower. The developed microchannel pattern was then used as a mold to develop the microfluidic devices. The polymethylsiloxane (PDMS) (Dow corning, Midland, MI, USA) microfluidic devices are fabricated by replica molding using the patterned silicon wafer. PDMS prepolymer and a curing agent were mixed in the ratio of 9:1, degassed, poured on the patterned-channel mold inside 10 cm petri dishes, and cured in a convection oven at 65 \({}^{\circ}\)C for  
Figure 3: Development of a finite element model for a single-cell trapping system: (**A**) Eulerian part (fluid channel’s top view) represented by main channel and trap channel (T1 + T2 + T3); (**B**) 3D deformable part (yeast cell model); (**C**) parts assembly showing the initial position of cell before simulation.  
2 h to complete the cross-linking. The cured PDMS was peeled from the mold and cut into individual devices. The holes for inlets and outlets were punched with a hollow needle 1 mm in diameter. Plasma oxidation (20 mA, 3 min) was used to activate the surfaces of PDMS and glass slides, followed by heating on a hot plate at 95 \({}^{\circ}\)C for 1 h to seal the PDMS and glass. Figure 4 shows a summary of the microchip fabrication steps.
# Hazard Model: Epidemic-Type Aftershock Sequence (ETAS) Model for Hungary
## 6 Parameter Downscaling Results  
### Downscaled Parameters  
The magnitude bounds are set at \(m_{0}=2.45\) and \(m_{x}=6.55\), and the magnitude class width is \(2\delta=0.1\). For simplicity, the same upper bound is used for all zones. Considering the maximum magnitudes of the model variants as in [PERSON] et al. (2016), \(m_{x}=6.55\) appears a conservative choice for the core geographical window. The largest homogenized magnitude in the historical catalogue observed within this area is \(M=6.1\) which is estimated for the Ermellek 1834 earthquake. The observation periods by magnitude class are given in Table 3.  
The downscaled parameter estimates by zone are shown in Table 4. The obvious boundary effects at the Croatian edge of the core window make the transformed time estimates unreliable in Zone 99. For this zone all original parameters other than \(\mu\) are kept unchanged. Because of data quality concerns, the parameters are not downscaled in the Romanian border Zone 05-06 either. As a placeholder solution for including the Romanian border area in the model, the kernel estimate of the background density \(u(x,y)\) and the declustering indicators \(\chi_{i}\) are extended to Zone 05-06 without changing the original ETAS parameters while applying a 0.5 credibility weight to all events with questionable data quality (that is, all events in zone 05-06 whose only source is Catalogue C) and restricting the estimation of \(\mu\) to the years 2000-2010. Another problem area is zone 13 on the northeastern periphery, where there is a very steep frequency decay at low-end magnitudes, which is a possible indication of the presence of an inhomogeneous sub-population. Therefore, in this zone, the lowermost magnitude classes--\(M<2.7\), for the estimation of \(\mu\), and \(M<2.6\), for transformed-time MLE--are excluded from the calculation. Thereby, some low-magnitude residual activity in this zone is left unmodelled.