esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Time-Frequency Transformation: Scalograms and Spectrograms
This permits the use of long time intervals where more precise low-frequency information is required, and shorter windows where high-frequency information is required. The wavelet analysis is then not only more appropriate for the analysis of transient signals but also for reducing noise in the process. In this work, a CWT was used. The CWT, unlike the DWT, lets the translation and scale parameters of the wavelets vary continuously, providing a complete representation of the signal. The scale parameter is the equivalent of the period parameter (the inverse of the frequency) in a periodic signal; it can stretch or shrink the wavelet with respect to its value. A shrunk scale can individuate slow changes in the signal while a stretched one localizes rapid transients in the analyzed signal. In mathematics, the CWT is a tool that gives a complete representation of a signal with the translation and scale parameter of the wavelets, which vary continuously. The CWT of a function \(x(t)\) at a scale \((a>0)\) with \(a\in\mathbb{R}^{+*}\) and a translational value \(b\in\mathbb{R}\) is expressed by the integral [45]  
\[X_{\omega}(a,b)=\frac{1}{|a|^{1/2}}\int\limits_{-\infty}^{\infty}x(t)\overline {\psi}\left(\frac{t-b}{a}\right)dt \tag{4}\]  
where \(\psi(t)\) is a continuous complex function in both the time and frequency domains (the so-called \"mother wavelet\"); the \(\psi(t)\) function in (4) is complex conjugated (denoted by the overline symbol). The principal purpose of the mother wavelet is to give a source function to generate the daughter wavelets, which are simply the translated and scaled versions of the mother wavelet. In this work, the CWT was processed through a filterbank in which 12 voices per octave were used; this means that there were 12 intermediate scale values for each octave. This discretization of the scale is not very high but it was considered to be suitable for the acceptable classification results achieved by the network. The wavelet used in the filter bank is the analytic Morse (3,60). This wavelet has a gamma value (symmetric parameter) of 3, which means it is symmetric in the frequency domain. The wavelet has a time-bandwidth product of 60. This parameter cannot exceed 40 times the gamma value; a higher value corresponds to greater spread of the wavelet in the time domain but also to a better frequency resolution. The length of the input signal windows and the sampling frequencies used have been varied for the specific cases analyzed and are reported in the following sections. More details on scalogram generation are presented in Section 4.1, where the first results are shown, although the same parameters are used in all the sections where a CWT was performed. Correspondingly, more details on the generation of spectrograms are reported in Section 4.4.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 5 Experimental Setup Parameters and Performance Metrics  
This section presents experimental setup parameters and performance metrics, including the end-to-end delay, and packet delivery rate.
###### Abstract  
This study focuses on microscale anisotropy in rock structure and texture, exploring its influence on the macro anisotropic electromagnetic parameters of the geological media, specifically electric conductivity (\(\sigma\)), relative permittivity (\(\epsilon\)), and magnetic permeability (\(\upomega\)). The novelty of this research lies in the advancement of geophysical monitoring methods for calculating cross properties through the estimation of effective parameters--a kind of integral macroscopic characteristic of media mostly used for composite materials with inclusions. To achieve this, we approximate real geological media with layered bianisotropic media, employing the effective media approximation (EMA) averaging technique to simplify the retrieval of the effective electromagnetic parameters (e.g., apparent resistivity-inversely proportional to electrical conductivity). Additionally, we investigate the correlation between effective electromagnetic parameters and geodynamic processes, which is supported by the experimental data obtained during monitoring studies in the Then Shan region. The observed decrease and increase in apparent electrical resistivity values of \(\rho_{k}\) over time in orthogonal azimuths leads to further \(\rho_{k}\) deviations of up to 80%. We demonstrate that transitioning to another coordinate system is equivalent to considering gradient anisotropic media. Building upon the developed method, we derive the effective electric conductivity tensor for gradient anisotropic media by modeling the process of fracturing in a rock mass. Research findings validate the concept that continuous electromagnetic monitoring can aid in identifying natural geodynamic disasters based on variations in integral macroscopic parameters such as electrical conductivity. The geodynamic processes are closely related to seismicity and stress regimes with provided constraints. Therefore, disasters such as earthquakes are damaging and seismically hazardous.  
electric conductivity; macro anisotropic electromagnetic parameters; anisotropy; bianisotropic effective parameters; homogenization; geologic fractured media; earthquake hazard analysis 2023
# Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges
## 7 Conclusions  
Movable bridges in different countries are heritage structures known for their beautiful architectural design and unique operating technique. They also serve an important purpose within the transportation system ecosystem, and their positioning is often vital in conflicting traffic modes. A comprehensive theoretical framework and algorithm for movable bridge maritime automation have been proposed and simulated in this research. The virtual belt division, buoy setup, and geographical positioning have been considered for Fort Madison Swing Bridge, and the design automation installments with division ranges have been accomplished. Different scenarios and notations have been demonstrated through simulation, CAD design, and the algorithm have been discussed accordingly. A counting algorithm has also been implemented to ensure bridge traffic control and automated navigation maintenance. By implementing these algorithms, an error-free and safe bridge evacuation and bridge reformation have been maintained. The purpose of this research is to enhance the reliability of the maritime management and control system in the movable bridge. The variation of velocity, acceleration, and distance among ships have been shown in curves in GUI for different phases, and this variation has been found analogous to the principle of ACC. The algorithm of the DSRC system for safety message command and arbitrary emergency control has been described, and the possible scenarios have been analyzed using the simulation study. In summary, this research will open new avenues in movable bridge research, incorporating automation and optimization to make regional and route planning smart and flexible [23; 24].  
Conceptualization: M.M.R.K. and M.S.I.S.; formal analysis, N.M.; original draft preparation: M.M.R.K., M.S.I.S. and N.M.; writing--review and editing: M.M.R.K. and M.S.I.S., N.M., J.P. and S.Q.L.; supervision: M.E., M.M., S.G. and S.Q.L.; project administration: M.E. and M.M. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the financial support of the National Natural Science Foundation of China under Grant No. 71871064 as well as the assistance from Queensland University of Technology in Australia, Fuzhou University in China, and Khulna University of Engineering Technology in Bangladesh.  
Not applicable.  
**Data Availability Statement:** Most datasets generated and analysed in this study are accessible in the website of [1].
## 2 Methods
### Calibration Resolution Concept  
Although it is now clear that the calibration curve for the ratiometric method can be prepared as a functioning lookup table within the convertible range as described in the previous section, the entire region in the convertible range does not guarantee uniform level of detection errors. This stems from the nature of the polynomial fit for the calibration curve, where a small change in the axial position of the point source \(z\) is not proportional to the change in \(I_{det}\). As shown by the yellow rectangles in Figure 4D, the identical intervals of \(\Delta C\) were not always converted to identically sized \(\Delta z\), along with the local third-order polynomial fit (the dashed line indicates an R-squared value of 0.9985).  
In order to investigate the accuracy distribution in the convertible range of the calibration curve, we can define the resolution of the plot, \(r\), as \(\Delta z\) for the identical \(\Delta C\), as shown in Equation (1).  
\[r=\Delta z=g(I_{det},\Delta C) \tag{1}\]  
where \(g\) represents the function mapping \(\Delta C\) to \(\Delta z\) defined by the acquired calibration curve, which essentially changes according to the value of \(I_{det}\). The \(r\) distribution in the convertible range is closely related to the differential of the calibration plot. For instance, as shown in Figure 4D, a relatively large change in \(\Delta C\) near the position where \(I_{det}\) was between \(-0.2\) and \(0\) returned a small change in \(\Delta z\), especially compared to the other areas in the convertible range. Smaller change in \(\Delta z\) represented low value of \(r\), which refers to the robustness in the conversion accuracy. Therefore, users could further refine the convertible range of the calibration curve to acquire finer accuracy in the conversion calculation, based on the size of the error allowable for their specific particle tracking task.  
One more fact we needed to consider when calculating the resolution \(r\) is that the value \(\Delta C\) largely depended on the measurement precision of \(I_{det}\) in the calibration curve, which could be theoretically estimated from the image resolution achieved for the two different focal planes. For example, if the image was taken with 8-bit resolution, the resolution of the \(I_{det}\) value was approximately \(\frac{1}{2^{8}}\), as a reciprocal of the possible maximum value of the denominator value, \(2^{8}+2^{8}\), in the \(I_{det}\) equation. Similarly, 14-bit images produced approximately \(\frac{1}{2^{15}}\) precision from a technical perspective.
# A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
## 2 Time Delay Mixing Model
Let the left-to-right columns of \(\mathbf{A}\) be numbered \(1,2,\cdots,K\). The numbering of the replacement is \(P_{1},P_{2},\cdots,P_{K}\),\(\phi_{k}=2\pi d\frac{\sin\phi_{k}}{\lambda}\). Then, \(\mathbf{A}\mathbf{P}^{-1}\) can be expressed as  
\[\mathbf{A}\mathbf{P}^{-1}= \begin{bmatrix}1&1&\cdots&1\\ e^{-j\phi_{P_{1}}}&e^{-j\phi_{P_{2}}}&\cdots&e^{-j\phi_{\mathcal{H}_{K}}}\\ \vdots&\vdots&\ddots&\vdots\\ e^{-j(M-1)\phi_{P_{1}}}&e^{-j(M-1)\phi_{P_{2}}}&\cdots&e^{-j(M-1)\phi_{P_{K}}} \end{bmatrix}  \tag{7}\]  
Substituting \(\mathbf{A}\mathbf{P}^{-1}\) and \(\mathbf{D}\) into Equation (6),  
\[\mathbf{\hat{A}}= \begin{bmatrix}1&1&\cdots&1\\ e^{-j\phi_{P_{1}}}&e^{-j\phi_{P_{2}}}&\cdots&e^{-j\phi_{\mathcal{H}_{K}}}\\ \vdots&\vdots&\ddots&\vdots\\ e^{-j(M-1)\phi_{P_{1}}}&e^{-j(M-1)\phi_{P_{2}}}&\cdots&e^{-j(M-1)\phi_{ \mathcal{H}_{K}}}\end{bmatrix} \begin{bmatrix} \begin{array}{cccc}\alpha_{1} ^{-1}&&\\ &\alpha_{2}^{-1}&&\\ &&\ddots&\\ &&&\alpha_{K}^{-1}\end{array} \\ = \begin{bmatrix}\alpha_{1}^{-1}&\alpha_{2}^{-1}&\cdots&\alpha_{K}^{-1}\\ \alpha_{1}^{-1}e^{-j\phi_{P_{1}}}&\alpha_{2}^{-1}e^{-j\phi_{P_{2}}}&\cdots& \alpha_{K}^{-1}e^{-j\phi_{P_{K}}}\\ \vdots&\vdots&\ddots&\vdots\\ \alpha_{1}^{-1}e^{-j(M-1)\phi_{P_{1}}}&\alpha_{2}^{-1}e^{-j(M-1)\phi_{P_{2}}}& \cdots&\alpha_{K}^{-1}e^{-j(M-1)\phi_{\mathcal{H}_{K}}}\end{bmatrix}  \tag{8}\]  
It can be seen that the elements of the first row of \(\mathbf{\hat{A}}\) contain exactly the amplitude phase ambiguity information of each signal. The amplitude phase ambiguity can be extracted from \(\mathbf{\hat{A}}\) to correct the estimated signal for amplitude phase. Thus,  
\[\mathbf{D}^{-1}=diag\big{\{}\mathbf{\hat{A}}(1,\,\cdot)\big{\}} \tag{9}\]  
After considering the noise, the output obtained by CVBSS is  
\[ \begin{array}{l}\mathbf{y}(t)=\mathbf{B}\mathbf{A}\mathbf{s}(t)+\mathbf{B}\mathbf{n}(t)\\ =\mathbf{D}\mathbf{Ps}(t)+\Delta\mathbf{s}(t)\end{array}  \tag{10}\]  
where \(\Delta\mathbf{s}(t)=\mathbf{B}\mathbf{n}(t)\) denotes the error due to additive noise. Therefore, the amplitude phase correction for CVBSS can be performed by left multiplying the matrix \(\mathbf{y}(t)\) by \(\mathbf{D}^{-1}\), i.e.,  
\[\hat{\mathbf{s}}(t)=\mathbf{D}^{-1}\mathbf{y}(t) \tag{11}\]  
Substituting \(\mathbf{y}(t)\) into Equation (11),  
\[\hat{\mathbf{s}}(t)=\mathbf{P}\mathbf{s}(t)+\mathbf{D}^{-1}\Delta\mathbf{s}(t) \tag{12}\]  
It can be seen that the error \(\Delta\mathbf{s}(t)\) still exists between \(\hat{\mathbf{s}}(t)\) and \(\mathbf{s}(t)\) due to the sequence ambiguity \(\mathbf{P}\) and additive noise. From the expression of \(\mathbf{A}\), for each column of \(\mathbf{A}\), the DOA corresponding to a source signal is obtained by \[\hat{\theta}_{k}=\frac{1}{M-1}\sum\limits_{i=1}^{M-1}\arcsin\biggl{(}-\frac{\lambda \arg\{\hat{a}_{k}(i+1)/\hat{a}_{k}(i)\}}{2\pi d}\biggr{)} \tag{13}\]  
\(\hat{a}_{k}(i)\) represents the \(i\)-th element in column \(k\) of \(\mathbf{A}\).
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 2 Proposed Method
### Computational Scheduling Kernel Pipeline Design
#### 2.2.2 On-Chip Storage Resource Bandwidth Expansion  
In the acceleration core, the convolutional scheduler needs to read a number of the input channel parallelism \(\times\) output channel parallelism parameters per clock, which are preloaded by the parameter loading kernel through the AXI bus into the BRAM. In response, the Xilinx HLS tool provides an array_partition constraint to break the BRAM into smaller sub-BRAM cells with more interfaces to guarantee access bandwidth. The specific role of the array_partition constraint is shown in Figure 9.  
A BRAM capable of storing nine words is instantiated into three BRAMs capable of storing three words after a memory decomposition constraint of factor 3, which expands the access bandwidth of the memory to three times the original one. In this paper, since the convolutional scheduling kernel needs to access the input channel parallelism \(\times\) output channel parallelism, and the BRAM bit width is 16 bits \(\times\) input channel parallelism, the storage decomposition factor is equal to the output channel parallelism number 8, thus decomposing one BRAM into 8 in parallel on the output channel so that the scheduling kernel can access all the required parameters simultaneously.
## 7 Performance Evaluation  
IoT applications such as in military, industrial, or smart-city are considered to illustrate the benefits of the proposed CPSO algorithm in determining the complex antenna array weights to best meet a specified far-field beampattern requirements and to reveal the advantages of the sensor selection algorithm for optimizing the beampattern. The proposed algorithm is implemented using MATLAB. Throughout the experiments implemented, we consider initializing the position matrix at the beginning of the experiment as linear, mesh or randomly distributed in a plane of area \(\mathbf{L}\times\mathbf{L}\,\mathbf{m}^{2}\). For example, \(\mathbf{L}=1200\) m is initiated and then we select the position matrix depending on the Sensor Array Analyzer toolbox of MATLAB to construct and analyze common sensor array configurations in terms different types of network topology. On the other hand, the overall network connectivity depends on various factors such as the channel randomness and the antenna model. Higher shadow fading variance leads to higher network connectivity, since it refers to the random variations of the received signal power around a mean value that is noticed when collaborative sensors are located at a given distance from a base station. Therefore, if we fix one of the other factors such as the directional antenna response in the desired direction to the base station, then we are able to achieve higher network connectivity to analyze the performance of CPSO against the conventional beampattern and the convex optimization in three different network topologies.  
The proposed CPSO algorithm tries to perturb (i.e., optimize) the complex weight coefficients and position of each sensor in turn. Indeed, we can say that it is possible that sparse antenna array elements with continuously spaced sensors could have a high degree of freedom in increasing and decreasing the mainlobe and sidelobes, respectively. Thus, the mainlobe of beamforming is included in the objective function Equation (29) with sidelobe constraints defined in Equation (30). The beampattern of 36, 64, 100, and 256 sensors deployed as equally spaced (uniform distribution) or  
Figure 6: The pattern for swarm deployment with various network topologies.  
non-uniformly spaced is calculated for each candidate sensor (i.e., solution) at each cost evaluation. The population is considered with the same number of deployed sensors, meanwhile the number of sampling of distributed collaborative sensors is varied depending on the network topology configuration. The values corresponding to the CPSO are initialized by performing parameters sensitivity test to the best values for \(\phi_{1}\), \(\phi_{2}\), and \(\omega\), whereas the \(\phi_{1}=\phi_{2}\in[0,4]\), and \(\omega\in[0,1.5]\). The initialized of parameters are iterated until they finally combined for updating the velocity using Equation (27) which is randomly initialized with values \([-1,1]\) and achieved the optimal solution. Table 2 lists the parameters used in the experiments
# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
## 3 The Construction Process of the Synthesis Health Index for Metro Vehicle Wheelsets
### Dimension Reduction of Metro Vehicle Wheelset Data
#### 3.1.2 Data Normalization of Metro Vehicle Wheelsets  
After feature selection, dimensionality reduction techniques can be employed to further decrease the data's dimensionality. However, prior to applying dimensionality reduction, the data must first be normalized. The two most commonly used normalization methods are Z-Normalization and Min-Max Normalization.  
Z-normalization helps eliminate differences in measurement units among various parameters of the wheelset profile data. Min-Max normalization constrains the data range, enabling all variables to be represented on the same scale, thereby enhancing subset representation [8]. In this paper, we first apply Z-normalization, followed by Min-Max normalization.  
The wheelset profile data exhibit a time series: \(X=\left(x_{i}\right)_{i=1:n}\). The time series of the data after normalization is \(Y=\left(y_{i}\right)_{i=1:n}\). The calculation formula for Z-normalization is:  
\[y_{i}=\frac{x_{i}-\mu(X)}{\sigma(X)} \tag{5}\]  
In this equation, \(\mu(X)\) represents the mean of the sequence and \(\sigma(X)\) denotes the standard deviation of the sequence.  
The calculation formula for Min-Max normalization is:  
\[y_{i}=\frac{x_{i}-\text{min}(X)}{\text{max}(X)-\text{min}(X)} \tag{6}\]  
In this equation, \(\text{min}(X)\) and \(\text{max}(X)\) represent the minimum and maximum values in the sequence, respectively.
# Investigating Attention Mechanism for Page Object Detection in Document Images
## 2 Related Work
### Page Object Detection
#### 2.1.2 Deep Learning Approach  
In terms of the deep-learning approach, the initial attempts toward page object detection were carried out by [PERSON] et al. [27] by building a GOD-framework to detect tables and figures. The model trains on a vast corpus of document images and obtains a trained model capable of distinguishing the graphical objects with good accuracy. Furthermore, a comparison is carried out between the traditional rule-based techniques and the GOD-framework to prove the effectiveness of the deep-learning approach in identifying and localizing document images, without any prior assumptions. Furthermore, the CasTabDetectoRS network [6] takes the task a step further by incorporating Cascade networks for identifying tables. It also introduced the concept of the Recursive Pyramid Network (R.P.N.), an extension of the well-known Region Proposal Network [10], and Switchable Atrous Convolutions [28]. Altogether, it experiments with a wide range of datasets and outperforms most models in detecting tables.  
In 2019, [PERSON] et al. [29] authored a YOLO-based table detection method. The contrasting difference between natural images and graphical objects is addressed in their work. An anchor optimization strategy is employed to identify anchor sizes that are optimized to fit the tables. This enables the model to find the exact locations of the tables, making it more robust. Fairly recent research by [PERSON] et al. [30] introduced RobusTabNet to localize tables and further proposed a split-and-merge method for recognizing the structure of a table by detecting the separation lines. This enables robust identification of the tables, in any orientation, e.g., warped orientation. They also introduced a new region proposal network called CornerNet to generate high-quality table proposals for the detection heads.
## 2 Materials and Methods
### Environmental and Remote Sensing Data  
Ambient air humidity (RH) and temperature (T) were monitored in each site using iButton Hygochron DS1923-F5# (iButtonLink, Whitewater, WI, USA) at a 5 h time step. The iButtons were placed at 2 m height on the north-facing side of a single tree in each plot. The period covered was between July 2020 and July 2021. For each month (i) and study site the average monthly temperature (Ti) and relative humidity (RHi) and standard deviation were calculated and compared between stands (see Figure 1). There were non-significant differences at ambient conditions among the sites in the same month. As the study plots are located within a small distance from each other, variation in Ti's and RHi's between plots are probably attributed to topographic variation and differences in stand structure. Since Ti and RHi data only partly overlapped with the simulation period, they were only used to describe site-specific weather conditions.  
Hourly data for direct normal irradiance and diffuse horizontal irradiance were downloaded from Solcast (Solcast, 2019, Global solar irradiance data and PV system power output data, URL [[https://solcast.com/](https://solcast.com/)]([https://solcast.com/](https://solcast.com/)) (accessed on 21 January 2021)), a global solar forecasting and historical solar irradiance data company. The two variables were summed up to calculate the total normal irradiance, as the maximum irradiance that canopy received. Total irradiance (Wm\({}^{-2}\)) was converted to PAR (umole quanta m\({}^{-2}\) s\({}^{-1}\)) by multiplying with a constant equal to 2.1479 [64]. In addition, absorbed canopy radiation (\(I_{c}\)) was estimated from total irradiance using a constant absorbance coefficient (a = 0.75) to account for the fraction of incoming solar radiation that is absorbed by the foliage [65].  
In order to compare our simulations with an independent dataset, we extracted for each study site the global GPP from the moderate resolution (500 m) dataset of [PERSON] et al. [66] for the 2000-2016 period. These estimates are based on an improved light use efficiency model and are driven by satellite data from MODIS and climate data from NCEP Reanalysis II. The mean value and the standard deviation (between years) across the 17 year-long period were used as a baseline to compare with our simulated GPP.