esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Limitations  
While our segmentation algorithm significantly improves on automatic segmentation methodologies, it is limited by the resolution limit of the MRI machine performing the MRA scanning. The CDF diameters (Figure 5) start at 0.5 mm because the distance map calculations determine radius from the edge of a blood vessel and a pixel in the MRA imaging represented 0.25 mm. Any value less than 0.5 mm would not be accurately represented due to the resolution limit. Subsequently, the accuracy of the statistical analysis decreases with decreasing blood vessel size (smaller blood vessels < 10 micrometers) above the circle of Willis).  
Various over-the-counter medications and supplements were used by the subjects over the time period of this study; however, the BP changes caused by these medications should be minimal. Nonetheless, larger sample sizes are required to establish definitive relationship with progression to HBP. Despite these limitations, our method is relevant to understanding brain pathology relevant to hypertension whether such pathology precedes or follows the establishment of clinical hypertension.  
Although it is hard to ask participants in a study that lasts for more than two years to visit the lab periodically to get their blood pressure measured, we suggest that taking blood pressure readings more frequently (maybe every three months) could provide more accurate observation of the MAP while excluding its possible temporal fluctuations, which could enhance the accuracy of this study.
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
###### Abstract  
The paper presents an analysis of ship traffic using the port of Swinoujscie and the problems associated with modelling vessel traffic flows. Navigation patterns were studied using the Automatic Identification System (AIS); an analysis of vessel traffic was performed with statistical methods using historical data; and the paper presents probabilistic models of the spatial distribution of vessel traffic and its parameters. The factors that influence the spatial distribution were considered to be the types of vessels, dimensions, and distances to hazards. The results show a correlation between the standard deviation of the traffic flow, the vessel sizes, and the distance to the hazard. These can be used in practice to determine the safety of navigation and the design of non-existing waterways and to create a general model of vessel traffic flow. The creation of the practical applications is intended to improve navigation efficiency, safety, and risk analysis in any particular area.  
vessel traffic flow; the safety of navigation; probabilistic model; Swinoujscie approach  
1 Faculty of Navigation, Maritime University of Szczeen, 70-500 Szczeen, Poland; [EMAIL_ADDRESS] (A.N.); [EMAIL_ADDRESS] (K.L.); [EMAIL_ADDRESS] (L.G.)  
2 Faculty of Maritime Studies and Transport, University of Ljublana, 6320 Portoroz, Slovenia; [EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (M.P)  
3
## 2 Formulation of the Problem
### Governing Equations
#### 2.2.1 Liquid Phase  
* Mass conservation equation: \[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=0\] (1) where \(x\) and \(y\) are Cartesian coordinate components. Moreover, \(u\) and \(v\) are velocity components in \(x\) and \(y\) directions, respectively.
* Momentum equation: \[\rho\left(\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}+v\frac {\partial u}{\partial y}\right)=-\frac{\partial p}{\partial x}+\mu\left(\frac {\partial^{2}u}{\partial x^{2}}+\frac{\partial^{2}u}{\partial y^{2}}\right)+S _{vx}\] (2) \[\rho\left(\frac{\partial v}{\partial t}+u\frac{\partial v}{\partial x}+v \frac{\partial v}{\partial y}\right)=-\frac{\partial p}{\partial y}+\mu\left( \frac{\partial^{2}v}{\partial x^{2}}+\frac{\partial^{2}v}{\partial y^{2}} \right)+S_{vy}\] (3) where \(\rho\), \(\mu\), \(p\), and \(t\) are density of water, viscosity of water, pressure, and time, respectively.  
Figure 1: Computational domain and coordinate system.  
* Energy equation: \[\rho C_{p}\left(\frac{\partial T}{\partial t}+u\frac{\partial T}{\partial x}+v \frac{\partial T}{\partial y}\right)=k\left(\frac{\partial^{2}T}{\partial x^{2 }}+\frac{\partial^{2}T}{\partial y^{2}}\right)+S_{h}\] (4) where \(C_{p}\), \(k\), and \(T\) are heat capacity of water, heat conductivity of water, and temperature, respectively. Moreover, the terms of \(S_{v}\) and \(S_{h}\) show the momentum and heat exchanges between two phases, respectively, and can be defined as [27; 28] \[S_{v}=\sum_{np}-\frac{m_{p}}{\delta V}\frac{d\ V_{p}}{dt}\] (5) \[S_{h}=\sum_{np}\frac{m_{p}}{\delta V}C_{p}\frac{dT_{p}}{dt}\] (6) where parameters with subscript of \"p\" are related to the particle phase. Accordingly, \(m_{p}\), \(n_{p}\), and \(\delta V\) are the particle mass, the number of particles in a cell volume, and the cell volume, respectively.
###### Abstract  
The time-interleaved analog-to-digital converter (TIADC) is an architecture used to achieve a high sampling rate and high dynamic performance. However, estimation and compensation methods are required to maintain the dynamic performance of the constituent analog-to-digital converters (ADCs) due to channel mismatches. This paper proposes a blind adaptive method to calibrate the nonlinear mismatches in \(M\)-channel TIADCs (\(M\)-TIADCs). The nonlinearity-induced error signal is reconstructed by the proposed multiplier Hadamard transform (MHT) structure, and the nonlinear parameters are estimated by the filtered-X least-mean square (FxLMS) algorithm. The performance of cascade calibration is also analyzed. The numerical simulation results show that the proposed method consumes much less hardware resources while maintaining the calibration performance.  
time-interleaved; analog-to-digital converter; nonlinear mismatch; binary Hadamard sequence; filtered-X least-mean square +
Footnote †: journal: Applied  
Article
## 2 Materials and Methods
### Metabolomic Profile Detection and Analysis  
For metabolome sequencing, fruits of strawberries FaHMGROE, FaHMGRR, and FaHMGRC were individually grounded in liquid nitrogen, and each homogenate was resuspended in pre-chilled 80% methanol and 0.1% formic acid and well vortexed before incubation. The samples were then centrifuged, diluted, and filtered before injection into the LC-MS/MS system. LC-MS/MS analysis was performed using a Vanquish UHPLC system (Thermo Scientific, Shanghai, China) coupled with an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher). Raw data were processed using Compound Finder 3.0 (CD 3.0, Thermo Fisher). Peaks were matched with the mzCloud ([[https://www.mzcloud.org/](https://www.mzcloud.org/)]([https://www.mzcloud.org/](https://www.mzcloud.org/)), accessed on 20 March 2022) and ChemSpider ([[http://www.chemspider.com/](http://www.chemspider.com/)]([http://www.chemspider.com/](http://www.chemspider.com/)), accessed on 20 March 2022) databases to obtain accurate qualitative results, with areas measured for quantitative analysis. The statistical analyses were conducted using R (R version R-3.4.3), Python (Python 2.7.6 version), and CentOS (CentOS release 6.6). Positive ion mode (POS) and negative ion mode (NEG) were both used to detect metabolites. The quality control (QC) sample was included to assess system stability, and a blank sample was used to eliminate background ions.
# Outlier Reconstruction of NDVI for Vegetation-Cover Dynamic Analyses
###### Abstract  
The normalized difference vegetation index (NDVI) contains important data for providing vegetation-cover information and supporting environmental analyses. However, understanding long-term vegetation cover dynamics remains challenging due to data outliers that are found in cloudy regions. In this article, we propose a sliding-window-based tensor stream analysis algorithm (SWTSA) for reconstructing outliers in NDVI from multitemporal optical remote-sensing images. First, we constructed a tensor stream of NDVI that was calculated from clear-sky optical remote-sensing images corresponding to seasons on the basis of the acquired date. Second, we conducted tensor decomposition and reconstruction by SWTSA. Landsat series remote-sensing images were used in experiments to demonstrate the applicability of the SWTSA. Experiments were carried out successfully on the basis of data from the estuary area of Salween River in Southeast Asia. Compared with random forest regression (RFR), SWTSA has higher accuracy and better reconstruction capabilities. Results show that SWTSA is reliable and suitable for reconstructing outliers of NDVI from multitemporal optical remote-sensing images.  
outlier reconstruction; tensor decomposition; tensor stream analysis; normalized difference vegetation index (NDVI); Salween River estuary
# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
## 2 Methods and Principles
### YOLOv5 Improvement
#### 2.2.2 ECA-Net Mechanism  
In order to improve the defect detection accuracy of the YOLOv5 algorithm in complex scenes, make the model focus on the object areas during training and suppress the ex  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Anchor1** & **Anchor2** & **Anchor3** \\ \hline \multirow{4}{*}{COCO} & (10, 13) & (30, 61) & (116, 90) \\  & (16, 30) & (62, 45) & (156, 198) \\  & (33, 23) & (59, 119) & (373, 326) \\ \hline \multirow{4}{*}{Ours} & (7, 12) & (34, 74) & (130, 54) \\  & (14, 59) & (47, 30) & (167, 257) \\ \cline{1-1}  & (16, 18) & (62, 161) & (380, 153) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of anchor parameters.  
pression of unimportant information, we introduced an ECA-Net mechanism [24] into the YOLOv5 model. ECA-Net is a type of lightweight attention module that has been improved on the basis of SENet [25]. It avoids dimensionality reduction and captures cross-channel interaction in an efficient way. The principle of the ECA-Net module is shown in Figure 2.  
We have the input feature map \(\chi\in R^{W\times H\times C}\) and output feature map \(\widetilde{\chi}\in R^{W\times H\times C}\) from the ECA module, as shown in Equation (3):  
\[\widetilde{\chi}=\sigma(g(\chi)*f)\otimes\chi, \tag{3}\]  
where \(g(\chi)\) is a global average pooling operation; \(f\) is a 1D convolution operation with \(3\times 3\) kernel size, which is then activated by the sigmoid function \(\sigma()\); and \(\otimes\) represents element by element multiplication with the original input features.  
When the feature maps are input into the ECA-Net module, the global average pooling of the feature maps is carried out channel by channel without dimensionality reduction, local cross-channel interaction is captured by paying attention to each channel and its k neighbors and then the weight of each channel is generated with the sigmoid function. This method has been proven to guarantee both efficiency and effectiveness. Finally, the original input features are combined with channel weights to obtain features with channel attention. The ECA-Net mechanism has fewer parameters, which can learn effective channel attention with low model complexity, and does not influence the detection speed of the algorithm to a large extent [26].
###### Abstract  
In this work, the experimental and simulation analysis of the performance of geopolymer composites reinforced with steel fiber and polypropylene fiber is investigated. By embedding hooked end steel fiber and polypropylene fiber with various volume fractions of 0%, 0.5%, 1%, 1.5% to the geopolymer concrete mixture, the mechanical behavior was enhanced significantly through experimental results. The compressive strength was improved 26% with 0.5% of polypropylene fiber and 46% with 1% of hooked end steel fiber while the increment of splitting tensile strength was 12% and 28%, respectively. The flexural strength of specimens using two fiber types was also improved when compared with the non-fiber geopolymer concrete. The highest increment obtained with 1.5% of fiber volume content was from 26% to 42%. The compressive performance and flexural performance of fiber-reinforced geopolymer concrete were also better than specimens without fiber, with a higher load carrying capacity, higher stress, higher toughness and smaller strain. Using hooked end steel fiber resulted in better mechanical strength than using polypropylene fiber, and the presence of fibers is an important factor related to the strength improvements. A finite element analysis was modeled by the ANSYS program, and this showed that the load-deflection response and crack patterns also agreed quite well with experimental results.  
geopolymer composites; compressive performance; flexural performance; hooked end steel fiber; polypropylene fiber; simulation analysis Article Assessment of Performance of Fiber Reinforced Geopolymer Composites by Experiment and Simulation Analysis
## 2 Methodology
### VISSIM Simulation
#### 2.3.2 Model Calibration  
Mean absolute percent error (MAPE), which gauges the error between the VISSIM model and reality, has been frequently utilized as an accuracy metric in calibration. Capacity and density are commonly examined indicators, with the calculations shown in Equations (2) and (3):  
\[MAPE_{\mathrm{C}}=\left(\sum_{i=1}^{n}C_{y}^{i}-\sum_{i=1}^{n}C_{f}^{i}\right) /\sum_{i=1}^{n}C_{f}^{i} \tag{2}\]  
\[MAPE_{\mathrm{K}}=\left(\sum_{i=1}^{n}K_{y}^{i}-\sum_{i=1}^{n}K_{f}^{i}\right) /\sum_{i=1}^{n}K_{f}^{i} \tag{3}\]  
where \(C_{y}^{i}\) and \(K_{y}^{i}\) denote the simulated capacity and density in the VISSIM model (veh/h) respectively, while \(C_{f}^{i}\) and \(K_{f}^{i}\) denote the investigated result, \(i\) denotes the traffic flow, and \(n\) denotes the total simulation times.  
Moreover, the Greenshields model will also serve as an additional benchmark to verify the simulation accuracy, which depicts the objective connection among the three traffic parameters in real traffic. As shown in Figure 6, volume increases towards its maximum capacity at a density equivalent to half of the jam density. Subsequently, volume experiences a decrease, eventually leading to the traffic flow being divided into smooth and congested areas.  
Figure 6: Connection between the three traffic parameters. \(K_{f}\) denotes the jam density, \(v_{m}\) denotes the critical speed, \(Q_{m}\) denotes the maximum volume, \(K_{m}\) denotes the optimal density, which always takes half of \(K_{j}\), and \(v_{f}\) denotes the smooth speed as density approaches 0.  
The VISSIM output will be fitted with Greenshields formula, and the goodness of fit (\(R^{2}\)) will be utilized to determine whether the simulated traffic flow matches the actual. This confirms the rationality and accuracy of the simulation setup.
## 3 Test Data Analysis
### Parametric Impact  
#### 3.2.1 pH Effect  
HNT adsorption upon pH arrangement. It influences the process as follows: it primarily affects surface features of the adsorbent and, secondly, BG chemical alterations [28]. The efficient utilization of selected adsorbents is exclusively influenced by pH. It is thus critical to have a thorough understanding of pH effect when commercialization of the process is desired [29]. The shape of the curve displaced in Figure 4a was consistent with the expected chemistry. The principal constituents of HNT are the oxides of aluminum and silicon. These oxides display different ionization properties and surface charges due to different zeta potentials when kept in contact with water [30]. The external surface of HNT showed negative charges due to silica above pH values of 2. Accordingly, the adsorption capacity of cationic BG dye decreased from pH 2 to 4. The pH has a profound influence on BG's structure and its characteristic feature as a cationic dye. Thus, with an increase in the pH, the BG dye loses its positive charge and also changes its color. A marginal rise in \(q_{e}\) from pH 6 to 12 is attributed to the positive charges of alumina in neutral and alkaline media, which attract the possibly negatively charged BG dye.  
Figure 3: FTIR spectra of BG dye, HNT, and BG-HNT.  
Figure 2: Halloysite nanotube surface characterization pre- and post-adsorption studies. (**a**) HNT pre-adsorption SEM image and (**b**) BG-HNT post-adsorption SEM image.  
Figure 4: _Cont._
# Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System
## 4 Demonstration HMI Design
### Stage 1--Notification  
With the goal being to notify the user when an event is detected, the notification screen (Figure 4) consists of two primary features, a list of all detected events and an infographic. The list is automatically set to have the most recent events at the top with the option to be reordered by clicking on any of the column headers; it contains some basic information about each event, including a unique event ID number, the time, date and location of the event, as well as the maximum contact force between the pantograph contact strip and the overhead line contact wire used to give the user a gauge of severity and urgency. Additionally, the list includes whether an arc was detected in the video footage, provided by a machine learning algorithm like that discussed in [26]. The infographic displays all of the locations where events have occurred and if there have been multiple events at the same location by increasing the size of markers with the number of events. Once the user clicks an event to further investigate, they then click on the green arrow to proceed to the second stage.  
In this way, the HMI for notification limits the amount of complex information about an event, minimizing the risk of information overload, and instead letting the user assess  
Figure 4: Notification HMI.  
its immediate relevance before they then decide whether to progress to the next level of investigation.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## 2 Literature Review
[27] proposed autonomous vehicular edge (AVE) for edge computing on the road to increase the computational capabilities of vehicles, and then extended this concept to the hybrid vehicular edge cloud (HVC), which enables the efficient sharing of all accessible computing resources. In summary, the above literature focuses on the design of the optimization objectives. The traditional objectives include minimum transport time, minimum cost and highest demand satisfaction. Emergency material should be delivered to disaster depots quickly and fairly, especially when the supply is insufficient. Here, fairness means that the delivery not only meets the demands averagely but also considers the damage situation of disaster depots. Moreover, the traffic capacity changes over time with secondary disasters and road repair. Therefore, it is essential to take into account the urgency of a disaster scenario when making decisions about emergency material scheduling. To address the above issues, we designed an optimal model (i.e., SEMS) considering that the relief supply cannot meet the demand of disaster areas with the objectives of maximum fairness and minimum time, where fairness considers the urgency of a disaster scenario evaluated by the data via V2X communications.
## 3 Test Results and Analysis
### Comparison with the Existing Researches  
Figure 14 compares the results of the experiment of [PERSON] and [PERSON] (1968) obtained through a model test and the results of experiment in this study using the batter angle of the pile and the relative density of the ground. While [PERSON] and [PERSON] (1968) represented the rate of increase of bearing capacity according to batter angle using a medium-level relative density, the experiment in this study segmented the relative density into the three levels of low density, medium density and high density. Though it was not possible to compare the values in low- and high-levels of relative density, this study obtained a value similar to that of [PERSON] and [PERSON] (1968) when the relative density was 53%.
# Monocular Facial Presentation-Attack-Detection: Classifying Near-Infrared Reflectance Patterns
## 2 Related Works
### Texture and Color-Based Spoof Detection  
Texture methods can theoretically detect facsimile artifacts. For example, printers can add distortion and quantize the colors of the intended face [36]. Likewise, both material and geometry can bias the way the spoof interacts with light; this can bias the distribution and, therefore, the perceived texture [36]. From a theory perspective, a well-scoped texture classifier should apply well to PAD.  
One of the popular facial-texture descriptors are local-binary-patterns (LBP) [37]. The LBP describe the local relative rate of change, or gradient, for a given image patch. This is particularly useful to generally differentiate between faces; however, [PERSON] et al. rather famously demonstrated LBP cannot differentiate liveliness on color imagery [13]. This is believed to be a shortcoming of using a passive RGB camera instead of an illuminated near-infrared camera.  
Color distributions can also theoretically identify spoof production artifacts [16]. [PERSON] et al. have demonstrated that ranked-channel histograms can in fact capture the color artifacts, but their results are only shown to work under static lighting. When reproducing their work, this research finds this approach to be too sensitive ambient lighting. This may also be another example of where using illuminated near-infrared can provide stability.  
The facial spoofing challenges demonstrates that texture methods can be enhanced by the used of sophisticated deep learning networks [25]. There are some exceptional methods presented that introduce sophisticated feature modeling techniques. Examples include utilizing unique convolutional kernels to better improve texture features [19], projecting spoof cues using generative techniques [20], and even inferring depth maps from 2D imagesusing multi-task learning techniques [21]. These algorithms are promising and do very well on the competition datasets. The concern, however, is feature-translation. One general problem with presentation attacks is they are easy to vary. Should the algorithm over-fit to features relevant to that specific presentations in the training dataset, they will not generalize to minor variations introduced as the attacks evolve (an issue known with DeepFake detection [38]. It is acknowledged that one solution is to do cross-dataset evaluation for experimental verification. The goal of this paper is instead to propose a physics-informed approach, which should intuitively should generalize well while retaining the goals of robustness and efficiency.