esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Challenges for Data Capture  
This study piloted a way of capturing data on the flow and attenuation of surface water in a mixed urban-rural environment. Recommendations from The Big SuDS Survey supported data collection on deployed SuDS' performance, to improve their future implementation [17]. It was hoped that data collected in this study could provide an evidence base for the performance of water infrastructure, demonstrating how that performance changed with time and with extremes of weather. However, whilst a significant difference was observed between the flow level in the manholes up and downstream of the SuDS features, the quality of information obtained was limited, due to a range of factors including experimental design and signal strength, which led to significant gaps in the data collected.  
Wireless telecommunications services are relied upon for communicating and accessing information, supporting applications such as remote sensing and telemetered data. These services depend on the quality of the enabling infrastructure, but signal obstacles, such as large buildings or valleys, (or in this case, manhole covers) can result in poor coverage. Furthermore, there is evidence to support a socio-economic divide in the quality of wireless telecommunication services, with poorer connectivity experienced in rural or lower-income areas than urban or affluent areas [42].  
Shortly after installation, it was noted that the telemetered unit was unable to push data to the UI in the community centre, due to extremely low signal. Several iterations of higher-gain external antennae were trialled, before opting to install a near-surface level high gain antennae to provide consistent signal for the instrumentation to transmit data. This involved cutting a small slot in the road surface and drilling into the manhole chamber to connect the telemetry unit to the road-mounted antenna, which was subsequently bonded and secured using suitable surfacing material. This solution, installed several months after the initial infrastructure, enabled the data to be transmitted and visualised in near to real time. Undertaking the installation of monitoring equipment was only possible due to the collaboration, but was time consuming. To alter physical infrastructure needed high level permission, especially to cut into the road surface. It is, therefore, very difficult to deploy and maintain monitoring equipment to routinely collect data on the effectiveness of SuDS.
_Algorithms_**2013**, \(6\), 12-28; doi:10.3390/a6010012  
_algorithms_  
ISSN **1999-4893**  
www.mdpi.com/journal/algorithms  
\(\ell^{1}\) Major Component Detection and Analysis (\(\ell^{1}\) MCDA):  
Foundations in Two Dimensions  
**[PERSON] \({}^{1,2,\ast}\), [PERSON] \({}^{1,3}\), [PERSON] \({}^{1,4}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC 27695-7906, USA; E-Mail: [EMAIL_ADDRESS] (Q.J.); [EMAIL_ADDRESS] (J.E.L.); [EMAIL_ADDRESS] (S.-C.F.)  
\({}^{2}\) School of Business Administration, Southwestern University of Finance and Economics, Chengdu, 610074, China  
\({}^{3}\) Department of Management Science and Engineering, Zhejiang University, Hangzhou, 310058, China  
\({}^{4}\) Mathematical Sciences Division and Computing Sciences Division, Army Research Office, Army Research Laboratory, P.O. Box 12211, Research Triangle Park, NC 27709-2211, USA  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +028-87092184; Fax: +028-87092768.  
_Received: 5 October 2012; in revised form: 3 January 2013 / Accepted: 7 January 2013 / Published: 17 January 2013_  
**Abstract:** Principal Component Analysis (PCA) is widely used for identifying the major components of statistically distributed point clouds. Robust versions of PCA, often based in part on the \(\ell^{1}\) norm (rather than the \(\ell^{2}\) norm), are increasingly used, especially for point clouds with many outliers. Neither standard PCA nor robust PCAs can provide, without additional assumptions, reliable information for outlier-rich point clouds and for distributions with several main directions (spokes). We carry out a fundamental and complete reformulation of the PCA approach in a framework based exclusively on the \(\ell^{1}\) norm and heavy-tailed distributions. The \(\ell^{1}\) Major Component Detection and Analysis (\(\ell^{1}\) MCDA) that we propose can determine the main directions and the radial extent of 2D data from single or multiple superimposed Gaussian or heavy-tailed distributions without and with patterned artificial outliers (clutter). In nearly all cases in the computational results, 2D \(\ell^{1}\) MCDA has accuracy superior to that of standard PCA and of two robust PCAs, namely, the projection-pursuit method of [PERSON] and the \(\ell^{1}\) factorization method of [PERSON]. (Standard PCA is, of course, superior to \(\ell^{1}\) MCDA for Gaussian-distributed point clouds.) The computing time of \(\ell^{1}\) MCDA is competitive with the computing times of the two robust PCAs.  
keywords:heavy-tailed distribution; \(\ell^{1}\); \(\ell^{2}\); major component; multivariate statistics; outliers; principal component analysis; 2D Classification:MSC 62H25, 65D10
# Research on Quantitative Investment Strategies Based on Deep Learning
## 4 Experimental Simulation
### Data Processing
#### 4.2.2 Calculating Implied Volatility  
Implied volatility is the volatility implied by the market price of an options. The most common method is to bring the current market price of the options into the Black-Scholes-Merton formula [34], and then to calculate the implied volatility in a reverse way.  
The Black-Scholes-Merton formula is one of the most famous and important formulas in financial engineering, and it has a major impact on how to price and hedge the options.  
For the non-dividend stock European options (the 50 ETF options in China's options market is one of them), the Black-Scholes-Merton formula is as follows:  
Call options:  
\[C\ =\ S_{0}N(d_{1})\ -\ Ke^{-rT}N(d_{2}) \tag{37}\]  
Put options:  
\[P\ =\ Ke^{-rT}N(-d_{2})\ -\ S_{0}N(-d_{1}) \tag{38}\]  
In the formula:  
\[d_{1}=\frac{\ln(\frac{S_{0}}{K})+(r+\frac{\sigma^{2}}{2})T}{\sigma\sqrt{T}} \tag{39}\]  
\[d_{2}=\frac{\ln(\frac{S_{0}}{K})-(r+\frac{\sigma^{2}}{2})T}{\sigma\sqrt{T}}=d _{1}-\sigma\sqrt{T} \tag{40}\]  
It should be noted that although \(d_{1}\) and \(d_{2}\) seem to be very cumbersome, they do have practical meaning. \(d_{1}\) describes the sensitivity of the options to the stock price (underlying asset), and \(d_{2}\) describes the likelihood that the options will be executed at last.  
In the formula mentioned above, \(C\) represents the European call options price; \(P\) represents the European put options price; \(S_{0}\) represents the starting price of the stock (underlying asset); \(K\) represents the strike price; \(r\) represents the risk-free interest rate of continuous compound interest; \(\sigma\) represents thevolatility; \(T\) represents the term of the options; \(N(x)\) represents the cumulative probability distribution function of the standard normal distribution.  
The deduction process of the implied volatility is as follows: after knowing the call options price \(C\), the underlying asset starting price \(S_{0}\), the strike price \(K\), the continuous compound interest risk-free interest rate \(r\), and the options term \(T\), the implied volatility of the options can be assumed as \(\sigma_{0}\). After bringing this into the Black-Scholes-Merton formula, the options price \(c\) under this implied volatility can be obtained. The term \(c\) is compared with \(C\). Since the options price increases along with the increase of volatility, if \(c<C\), the volatility is set to be \(\sigma_{1}\), where \(\sigma_{1}>\sigma_{0}\). This is brought into formula (34) or (35) to obtain the price of the options under \(\sigma_{1}\), compared with \(C\). In this way, the approximate solution of the implied volatility can be obtained. In this paper, the fiero function is directly called in MATLAB to solve the inverse calculation process of volatility.  
The experimental results will be analyzed below.
## 3 Construction of OD Passenger Flow Prediction Deep Learning Model  
### Temporal Convolutional Network Model  
A temporal convolutional network (TCN) utilizes convolutional operations to capture local dependencies in a sequence and construct a deep network structure by stacking multiple convolutional layers, which provides a new method of hierarchical capture of space and time. The time convolutional network model has three main characteristics:  
1. TCN uses causal convolution. Since each prediction can only rely on previous data predictions, there will be no data leakage. The principle of causal convolution is shown in Figure 4. The value of the previous layer at time t only depends on the value before the next layer at time t; thus, causal convolution cannot predict future values. Causal convolution has a unidirectional structure and strict constraints on timing, and too many convolutional layers will lead to gradient disappearance and a poor fitting effect.
2. TCN combines deep neural networks with dilated convolutions to form a model that can save long-term valid historical data. It can effectively improve the performance of the model and solve the problems of causal convolution. Expanding convolution mainly allows the filter to be applied to filer regions exceeding its own length range by ignoring some inputs, which is equivalent to adding zero to obtain a larger filter from the initial filter. The principle of the expanding structure is shown in Figure 5.
3. The TCN model introduces a residual network to build long-term dependencies, thereby effectively improving the model's performance. The structure principle of the residual network is shown in Figure 6. To some extent, the residual connection overcomes the problems caused by the disappearance of local gradients and edge gradient explosions in convolutional neural networks to achieve cross-layer information input.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Illustrations** & **Variables** & **Illustrations** \\ \hline \multirow{3}{*}{\(Y_{1}\)} & Date attribute (1–7 represents Monday to Sunday, respectively) & \multirow{3}{*}{\(Y_{3}\)} & Time granularity/hour \\ \cline{1-1} \cline{3-4}  & Air quality/non-dimensional relative numerical values & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Influential parameters of passenger flow analysis prediction.  
Figure 4: Causal convolutional structure.
## 2 Data Preprocessing  
### Sample Selection  
This study draws on data from the Stanford Earthquake Dataset (STEAD), a globally recognized labeled seismic dataset. From this dataset, local seismic waveforms were selected, each containing 35 attributes (labels). These primarily include the receiver network code, receiver code, receiver type and location, source time function, epicenter location, magnitude type, and arrival times of P-waves and S-waves. Objective attributes such as the seismic station location were excluded, and an analysis of magnitude correlation was conducted. Through feature selection, the data dimensionality was reduced to save costs and enhance the performance of the classification model. In this experiment, the results of the Kendall rank correlation coefficient were used to determine the final experimental parameters as shown in Table 1, which consisted of four attributes: the magnitude, P-wave arrival time, S-wave arrival time, and end time. The absolute values of the correlation coefficients between the latter three attributes and magnitude were 0.67, 0.75, and 0.86, respectively.  
Earthquakes with a magnitude of 3.0 or lower are generally categorized as microearthquakes or weak earthquakes. Given that the accuracy of magnitude classification decreases significantly when the magnitude difference exceeds one unit, this study focuses  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Receiver Latitude** & **Receiver** & **Wave Uncertainty** & **P-Wave** & **P-Wave Arrival** \\  & **Longitude** & **Weights** & **Propagation Time** & **Time** \\ \hline Kendall rank & & & & & \\ correlation & 0.11 & 0.04 & 0.25 & 0.28 & \(-\)0.67 \\ coefficient & S-wave uncertainty & S-wave arrival & Source level & & \\  & weights & time & uncertainty time & & \\ Kendall rank & \(-\)0.45 & 0.75 & 0.15 & 0.36 & \(-\)0.14 \\ coefficient & & & & & \\  & Time residual & Maximum azimuth & Seismic level & & \\  & & clearance & uncertainty & Focal depth & \\  & & & kilometers & & kilometers \\ Kendall rank & & & & & \\ correlation & 0.31 & 0.25 & 0.46 & \(-\)0.36 & \(-\)0.25 \\ coefficient & & & & & \\  & Source stop time & Focal angle & Hypocentral & & \\  & & distance & & & \\ Kendall rank & & & & & \\ correlation & \(-\)0.86 & \(-\)0.33 & 0.22 & 0.14 & 0.27 \\ coefficient & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results table of seismic characteristic correlation coefficient.  
on predicting microearthquake magnitudes to better capture seismic events with subtle waveform differences. A total of 9000 microseismic signal waveforms with a magnitude of 3.0 or lower were selected. These samples were derived from associated waveforms in continuous time series archived by the Data Management Center of the Seismological Cooperative Research Society [16]. Each waveform starts 5 to 10 s before the P-wave arrival and ends at least 5 s after the S-wave arrival.  
In the waveform images, the arrival times of P-waves and S-waves, as well as the end time of the waveform, were marked to facilitate subsequent magnitude classification based on the images. The ratio of the training set to the test set was 8:2.
# Failure Assessment of Embankment Dam Elements: Case Study of the Pirot Reservoir System
## 4 Numerical Model of the Zavoj Dam  
### Finite Element Model  
For developing a 3D model of the Zavoj dam, the available 2D drawings [36] were used as the base. The created 3D model was the basis for developing the finite element model of the dam and surrounding rock mass using Femap program [37]. The model was developed on the basis of geometry, engineering-geological structure of the terrain, and exploitation conditions in accordance with the requirements of numerical solvers. Numerical analyses of filtration processes and strength were performed using the software package PAK [14] in which the methods presented in the Theoretical Basis section were implemented.  
The boundaries of the finite element model have been moved further from the dam structure (about 3.5 and 1.5 times the dam height away from the dam body) in order to reduce the influence of boundary conditions on the numerical results of filtration and strength analysis (Figure 4).  
Figure 3: Zavoj dam—photo of the construction site.  
To create the finite element model of the dam with the surrounding rock mass, tetrahedral finite elements with midside nodes (10-node per finite element) were used. The developed model of the dam consists of approximately 98,000 finite elements and about 140,000 nodes. During the development of the numerical model, special attention was paid to the detailed modeling of the dam structure with collapsing elements. The following groups of structural elements were formed, representing quasi-homogeneous zones:  
* Dam body: \(\circ\) Clay core, \(\circ\) Multilayer sand filters, \(\circ\) Upstream dam body, \(\circ\) Downstream dam body,
* Grouting curtain, \(\bullet\) Concrete spillway, \(\bullet\) Surrounding rock mass.
## Materials and Methods
### Methods
#### 2.4.2 Relationship Between NDVI and NDRE and Leaf Water Content and Water Potential  
NDVI and NDRE were calculated from each UAV image mosaic using standard formulas. All data from all flights were clipped to the boundaries of the study area, described above, and either left at their native resolution or resampled to a common resolution. The values for all pixels within the delineated boundaries for each treatment were averaged to provide a mean value for each treatment at each time period. For the control (untreated) plots, pixels were averaged within the boundary of each plot, and then averaged overall (Table S1). NDVI and NDRE values for the 1 pm flight were plotted with field-measured leaf water content measured at 1 pm for both Day 1 and Day 2. The average NDVI and NDRE values for all flights (n = 12) were also plotted against the average of water content over the two days. Correlations between these values were assessed with the nonparametric Spearman correlation coefficient. Additionally, we assessed the relationship between leaf water content and leaf water potential with a linear regression. Finally, to determine significant difference between the treatment values, we ran a nonparametric Wilcox test to distinguish pairwise significant differences  
Figure 2: (a) NDVI values and (b) NDRE values per treatment for flights with GSD of 2.2 cm, 4.3 cm, 7.2 cm, and 8.7 cm (flying height of 30m, 60m, 100m, and 120m); and (c) spectral index value per treatment over the course of eight flights in one day (resampled to 4.3 cm resolution). NDVI: normalized difference vegetation index. NDRE: normalized difference red edge index.  
between each treatment. The Wilcox test is a statistical comparison of the average of two dependent samples and a nonparametric version of a t-test that assesses the difference between observations [54].
## 4 Results: Path Loss
### Validation: 3D Heterogeneous Human Frontal Thorax Model vs. Equivalent Tissue Layer  
This section is based on the configuration explained in Section 2.3 and is used for carrying out the simulations in a 3D heterogeneous adult model. The results from these simulations are validated by calculating the PL in tissue layers that are equivalent to the human frontal thorax using the simplified PL model. The thicknesses and the dielectric properties of the tissue layers are as mentioned in Section 2.3.  
Figure 8 shows the comparison of the calculated PL model in equivalent tissue layers and the simulated PL in Duke _versus_ the distance between the Tx and the Rx antenna. In the heterogeneous human body, the receiving antenna moves through the various layers, and PL is obtained. The layers in the frontal thorax of Duke for skin, SAT and breast tissue are lesser in thickness (2 mm, 5 mm and 1 mm) compared to the thickness of the insulated antenna (5 mm). Thus, when the antenna is placed in a specific layer, the layers that surround the antenna are not similar to the layer in which it is placed. Thus, a deviation can be observed between the simulated and calculated PL and also a change in the standing wave pattern between the calculations and the simulations. The standing wave pattern observed in the Figure 8 is due to the reflections caused by the insulation of the antenna. There is a maximum deviation of 1.9 dB and an average deviation of 1.7 dB between the calculated PL model of Equation (4) and the PL from simulation.  
Figure 8: Simulated PL in the heterogeneous human Duke and PL model _versus_ the separation distance between antennas.  
For FDTD simulations carried out in Duke, the time required for a sample size of 30 antenna separations is 86,400-times more than that required for calculation using the PL model, _i.e._, it takes approximately five days to carry out the simulations, while the calculations using the simplified PL model takes only five seconds.
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 2 Methodology
### Boundary Conditions  
The flow medium consists of a slurry and bubbles, with the slurry density set to 1300 kg/m\({}^{3}\) and viscosity set to 0.001 kg/(m\(\cdot\)s). Bubble density is set at 1.225 kg/m\({}^{3}\) with a bubble size of 0.001 m. Gravitational acceleration is assumed to be 9.81 m/s\({}^{2}\).  
In the simulation of the flow field within the jet-mixing tank, the boundary conditions are specified as follows:  
1. Inlet boundary conditions: The jet inlet is set as a velocity inlet. For a total feed volume of 5.64 m\({}^{3}\)/h, each single nozzle receives 1.41 m\({}^{3}\)/h, corresponding to a velocity of 5 m/s. The volume fraction of the gas phase is fixed at 0.25, meaning that in the total flow rate of 5.64 m\({}^{3}\)/h, 25% is gas and the remaining 75% is liquid. Therefore, the gas flow rate is 1.41 m\({}^{3}\)/h (5.64 m\({}^{3}\)/h \(\times\) 0.25), and the liquid flow rate is 4.23 m\({}^{3}\)/h (5.64 m\({}^{3}\)/h \(\times\) 0.75).
2. Outlet boundary condition: The outlet boundary adopts a pressure outlet with a pressure value of 0 Pa. The outlet boundary backflow direction specification method is set to normal to boundary.
3. Wall boundary condition: A no-slip boundary condition was applied at the wall. Standard wall functions were employed in the turbulence model, with the distance from the wall to the nearest computational node (\(\delta\)) set to 0.6 mm to ensure that the first computational node is located within the logarithmic region of the boundary layer.
Applied Sciences  
Article  
Spatial Distribution, Risk Index, and Correlation of Heavy Metals in the Chuhe River (Yangtze Tributary):  
Preliminary Research Analysis of Surface Water and Sediment Contamination  
[PERSON] 1,2,*(c), [PERSON] 1,2, [PERSON] 1,2(c) and [PERSON] 3  
1 Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CIC-AET), Nanjing University of Information Science & Technology, Nanjing 210044, China;  
[EMAIL_ADDRESS] (R.T.M.); [EMAIL_ADDRESS] (E.N.)  
2 School of Geography and Environmental Science, University of Reading, Reading RG6 7 BE, UK  
3 Department of Life Sciences, Imperial College London, London SW7 2 AZ, UK; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS]  
This comprehensive study aimed to evaluate the water quality and sediment contamination in the Chuhe River in Nanjing. The spatial assessment of 10 samples collected in September highlighted that, in surface water, Copper (Cu) > Nickel (Ni) > Zinc (Zn) > Chromium (Cr) > Lead (Pb) > Arsenic (As) > Cadmium (Cd) > Mercury (Hg), whereas in sediments, Zn > Cr > Cu > Pb > Ni > As > Cd > Hg. The coefficient of variation (CV) for Ni and Zn in surface water was >15, whereas As, Cu, Pb, and Ni had a CV that was higher than 15 in sediments, indicating variability in contamination sources. The Pollution Load Index values ranged between 2.16 and 3.05, reflecting varying contamination levels across samples. The Geosc accumulation Index data also showed moderate-to-considerable contamination, especially for elements such as Cd and Cu. Correlation analyses in water and sediments unearthed significant relationships, with notable links between Cu and Pb in the water and strong correlations between As and Cu and between Cr and Ni in sediments. In sediments, Total Nitrogen and Phosphorus were significantly correlated with As, Cu, Pb, and Ni. The Potential Ecological Response Index for sediments indicated that they are at medium to high risk (307.47 \(\pm\) 33.17) and could be potentially detrimental to aquatic life in the tributary. The tributary, influenced by agricultural runoff, residential areas, and other anthropogenic activities, showed that despite Nemerow pollution index values for water samples being below 1, sediment analysis indicated areas of concern. Principal Component Analysis (PCA) was conducted to identify the potential sources of heavy metal contamination. In surface water, shared negative loadings on PC 1 (60.11%) indicated a unified influence, likely from agricultural runoff, while PC 2 (14.26%) revealed additional complexities. Sediments exhibited a unique signature on PC 1 (67.05%), associated with cumulative agricultural impacts, with PC 2 (18.08%) providing insights into nuanced factors, such as sediment composition and dynamic interactions. These findings offer a complete insight into the Chuhe River tributary's condition, underlining the urgency for ongoing monitoring and potential remediation measures.  
Yangtze tributary; metal concentrations; Nemerow pollution index; Geoscumulation Index (Igeo); Pollution Load Index (PLI)