esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Octonion Cross Wigner Distribution of 3-D Signals
## 4 Examples
Choosing other parameters of Gabor's signals, one can draw similar conclusions as in the previous experiment--differences in functions in each direction result in the appearance of non-zero values in the corresponding imaginary parts (changes in the \(x_{i}\) direction cause the appearance of the \(\mathbf{e}_{2^{i}}\) component, changes in the two directions \(x_{i}\) and \(x_{j}\) cause the appearance of imaginary components \(\mathbf{e}_{2^{i}}\), \(\mathbf{e}_{2^{j}}\), and \(\mathbf{e}_{2^{i}}\cdot\mathbf{e}_{2^{j}}\)). Changes in all directions result in the appearance of all imaginary components. Moreover, the variable \(x_{k}\) used in the sine carrier also influences the appearance of additional imaginary components (\(\mathbf{e}_{2^{i}}\), \(\mathbf{e}_{2^{i}}\cdot\mathbf{e}_{2^{k}}\), \(\mathbf{e}_{2^{j}}\cdot\mathbf{e}_{2^{k}}\), and \(\mathbf{e}_{2^{j}}\cdot\mathbf{e}_{2^{j}}\cdot\mathbf{e}_{2^{k}}\)). There is a clear difference between this approach and the classical one, where changes result in the appearance of the imaginary part, but since there is only one imaginary part, it is not possible to immediately extract information about the direction of change from the analysis of the Wigner distribution alone.
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 2 Background of Technologies behind SDVC
### Software-Defined Network and Its Concept for Vehicular Networks
SDN suggests separation of data plane from the control plane with well-defined programmable interfaces to provide a centralized global view of vehicular networks. SDN defines an easier way to configure and manage vehicular  
Figure 4: Logical View of Software-defined Network. networks. In the same manner, a centralized system has better consistency since the SDN controller has a global view of the network. SDN has evolved in design of next generation of vehicle networks [33]. The SDVN pioneers the concept of SDN in vehicular networks [33]. The control plane is responsible for collecting and maintaining the network management of all SDN enabled radio access at RSUs and vehicles. An example of an application based on SDVN is the prediction of optimal driving paths on demand. The application layer in SDVN architecture could monitor vehicles on the roads, and provide additional best navigation paths at certain times of the day or when the vehicles are temporarily disconnected due to the high road traffic. The ability to deploy an important number of vehicular applications through SDVN concepts brings the true meaning of the Intelligent Transportation System (ITS). Today, deployment of ITS applications demands higher agility in network restoration, massive scalability, faster deployment, and operating expenses optimization. ITS services simply cannot afford to be slowed down by the lack of speed in human-driven processes. Automation and programmable capabilities are needed to support the provisioning of ITS services, the monitoring of ITS networks, implementation of run-time changes due to the high mobility of vehicular networks and road traffic loads, etc. In addition, SDN offers a solution by linking ITS services to the vehicular network and reducing human intervention in the process of network management. The SDVN allows vehicular routing protocols to react to a dynamic environment and mobility of the vehicles. Furthermore, SDVN relieves the burden of open protocols to manage ITS applications. In fact, these open sources would run on top of the SDN controller through the northbound Application Programming Interface (APIs). Therefore, the features of SDVN would mitigate the issue of high mobility of vehicles, satisfy the requirements of ITS services, and enable heterogeneous radio access in the next generation vehicular networks.
## 4 An Experimental Case Study
### Wind Tunnel Experiments
As reported in important and interesting contributions [15; 16] on structural health monitoring (SHM), the effective detection of changes in these modal parameters results in a reliable indication of the physical conditions of the systems under analysis. In addition, a desirable feature of structural monitoring techniques is a fast performance with a limited number of sensors and as little post-processing as possible. The identification scheme evaluated in this work achieves those characteristics and is feasible for its application in real-time and online structural monitoring schemes. Finally, the real time and online determination of the mode shapes with the evaluated algebraic scheme, will be a natural extension, part of future works, in the context of structural health monitoring (SHM) applications. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Frequency [Hz]** & \\ \hline
**Mode** & **Off-Line** & **On-Line** & **Difference \%** \\ \hline
1 & 9.15 & 9.26 & 1.29 \\
2 & 32.59 & 33.40 & 2.51 \\
3 & 74.45 & 72.51 & 2.6 \\
4 & 141.0 & 145.23 & 3.0 \\
5 & 175.1 & 178.25 & 1.8 \\
6 & 213.24 & 210.04 & 1.5 \\ \hline  & & **Damping Ratio \%** & \\ \hline
**Mode** & **Off-Line** & **On-Line** & **Difference \%** \\ \hline
1 & 1.5 & 1.55 & 2.9 \\
2 & 0.9 & 0.85 & 5.1 \\
3 & 1.43 & 1.25 & 1.26 \\
4 & 2.8 & 2.88 & 3.0 \\
5 & 2.0 & 2.16 & 7.8 \\
6 & 1.8 & 1.74 & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results summary and comparison. Figure 14: Convergence of the sentinel parameter. Figure 15: Algebraic and on-line estimation of natural frequencies. In the spectrogram shown in Figure 17, it is possible to observe that for \(t>18\) s, the frequency components of the signal vanish according to the slow and gradual decay in the velocity response. Figure 16: Algebraic and on-line estimation of damping ratios. The dominant mode for this particular case (velocity measurements) is the one centred at 74 Hz, which is dominantly linear and time invariant for the particular turbulent wind speed of 20 m/s. The spectrogram shown in Figure 17 also reinforces the conclusion of the linearity assumption of the dynamic behaviour of the blade in those wind speed conditions, numerically quantified by the evaluated non-linearity index defined by (27) and the numeric result given by (29).
## 4 Voltage Vector Distribution Method
### Voltage Vector Over Range Judgmental Algorithm  
The voltage vector over range judgmental algorithm is derived from the SVPWM strategy. It is used to calculate the proportions of adjacent basic vector for synthesizing desired voltage vector of a single inverter [25]. The calculation result can also indicate whether the desired voltage vector is in the modulation range, which is what we need here.  
The desired voltage vector is given in static two-phase coordinate, which are D-axis component \(u^{*}_{D}\) and Q-axis component \(u^{*}_{Q}\). As shown in Figure 5a, axis \(J\), \(K\) and \(L\) are distributed in circular uniform. The projection of the desired voltage vector on axis \(J\), \(K\) and \(L\), defined as \(j\), \(k\) and \(l\) respectively, can be obtained by Equation (14):  
\[\left\{\begin{array}{l}j=\frac{u^{*}_{Q}}{2}\\ k=\frac{\sqrt{3}u^{*}_{D}}{2}-\frac{u^{*}_{Q}}{2}\\ l=-\frac{\sqrt{3}u^{*}_{D}}{2}-\frac{u^{*}_{Q}}{2}\end{array}\right. \tag{14}\]  
Figure 5: Sector partition and voltage vector synthesizing of space vector pulse width modulation (SVPWM). (**a**) Sector partition; (**b**) voltage vector synthesis.  
Then the sector number \(N\) can be determined as follow:  
\[N=sign(j)+2 sign(k)+4 sign(l) \tag{15}\]  
where \(sign\) is the sign function, we have \(sign(x)=1\) if \(x>0\), otherwise we have \(sign(x)=0\). There are six sectors in total, and the sector number is represented by roman numerals shown in Figure 5a. The proportion parameters \(x\), \(y\) and \(z\) can be calculated using \(u_{D}^{*}\) and \(u_{Q}^{*}\) by Equation (16), where \(V_{dc}\) is the DC-bus voltage of the corresponding inverter.  
\[\left\{\begin{array}{l}x=\frac{\sqrt{2}u_{Q}^{*}}{V_{dc}}\\ y=\frac{2u_{D}^{*}+\sqrt{3}u_{Q}^{*}}{\sqrt{6}V_{dc}}\\ z=\frac{-2u_{D}^{*}+\sqrt{3}u_{Q}^{*}}{\sqrt{6}V_{dc}}\end{array}\right. \tag{16}\]  
Afterwards, the proportions of adjacent basic vectors to synthesize desired voltage vector, defined as \(a\) and \(b\), can be obtained by distributing the three proportion parameters according to the sector number, which is shown in Table 1.  
As shown in Figure 5b, taking inverter1 as an example, the desired voltage vector \(\vec{u_{s1}^{*}}\) lies in Sector III, and is synthesized by the adjacent basic vectors \(\vec{u_{1}}\) and \(\vec{u_{2}}\). We have:  
\[\vec{u_{s1}^{*}}=\vec{a\vec{u_{1}}}+\vec{b\vec{u_{2}}} \tag{17}\]  
\(a+b\leq 1\) indicates the desired voltage vector is within the range of the hexagon and can be modulated. Otherwise when \(a+b>1\), it means the desired voltage vector is out of the modulation range and cannot be generated integrally.
## 3 Methodology
### Data Pre-Processing
#### 3.1.3 Pearson Correlation Analysis  
This study investigates the influence of weather variables on electricity load. The linear regression model assesses the interdependence between variables using Equation (2). In this equation, \(\gamma\) represents the dependent variable (electricity load), \(X\) denotes the independent variable (weather variable), \"\(a\)\" is the y-intercept, and \"\(b\)\" is the slope of the regression line.  
\[y=ax+b \tag{2}\]  
To further quantify the strength and direction of the linear relationship between weather variables and electricity demand, [PERSON]'s correlation coefficient is utilized. Higher absolute values of the correlation coefficient (closer to 1) indicate a stronger, linear relationship, while values closer to 0 suggest a weaker, linear relationship. Mathematically, [PERSON]'s correlation coefficient (\(r\)) is defined as Equation (3).  
\[r=\frac{m\sum pw-\sum p\sum w}{\sqrt{\left\{n\sum x^{2}-\left(\sum x\right)^{2 }\right\}\left\{n\sum y^{2}-\left(\sum y\right)^{2}\right\}}} \tag{3}\]  
where \(m\) represents the number of data pairs, \(\sum pw\) is the sum of the products of paired scores (p multiplied by w for each data point), \(\sum p^{2}\) is the sum of squared p, and \(\sum w^{2}\) is the sum of squared w.  
The strength of the correlation between variables is interpreted based on the absolute value of the Pearson's correlation coefficient (\(r\)) as detailed in Table 2. Positive r values (0 to 1) indicate a positive correlation, while negative r values (\(-1\) to 0) indicate a negative correlation [45; 46]. This study only considers variables with strong correlations to electricity load in the model development. This approach aims to avoid the \"complexity dilemma\", where introducing weakly correlated variables can unnecessarily increase model complexity without improving forecasting accuracy.
# Design of Novel Cooling Systems Based on Metal Plates with Channels of Shapes Inspired by Nature
## 3 Heat Transfer Configurations  
The cooling systems analyzed consist of an aluminum plate with a channel drilled inside. The properties considered for the aluminum plate for the heat transfer computations are density \(\rho=2650\) kg/m\({}^{3}\), specific heat \(c_{p}=890\) J/(kg\(\cdot\)K), and thermal conductivity \(k=113\) W/(m\(\cdot\)K). All the plates are squared and have the same dimensions, with a length of \(L=15\) cm and a thickness of \(s=1\) cm. The cross section of the channel is circular, with a diameter of \(d=6\) mm, and the length of the channel is \(L_{\rm ch}=43.9\) cm for all configurations. The shape of the channel along the plate differs for the different configurations analyzed. However, the diameter and length of the channel are the same for all configurations to allow the direct comparison of different cases. The channel is located at the middle plane of the plate thickness, with its inlet and outlet centered in a side of the plate and separated 3.75 cm for all cases studied. Therefore, the external heat transfer surfaces of the plate in contact with ambient air and the internal heat transfer surface of the channel in contact to the cooling fluid are constant for all the configurations considered.  
A standard configuration for a channel of a cooling system over a surface is a squared shape, keeping a constant distance to all sides of the surface. This square shaped channel can be observed in Figure 2, where the channel is placed 1.75 cm apart from the plate sides. Another usual configuration for the channel is that typically used for electric resistors, with a fork shape. A smooth fork shape channel configuration, keeping the same inlet, outlet, and length of the channel as the square configuration, is also included in Figure 2.  
Figure 1: Flow chart of the finite difference schemes applied (blue lines correspond to the transient cooling calculations and red lines to the steady state refrigeration).  
The performance of the traditional configurations presented in Figure 2 was compared to that of the novel designs inspired by the shapes of the outline of flowers. The novel configurations of the cooling system channel were named after the shape of the flowers that inspired their design, using the botanic terminology proposed in Hickey and King [48]. Figure 3 shows the three novel configurations proposed, which include specifically crateriform, salverform, and cruciform shapes for the channel of the cooling system. All the configurations based on flowers' outlines kept the same inlet, outlet, diameter, and length of channel as the common configurations depicted in Figure 2 so that the surface of the plate in contact with the cooling fluid is the same for all configurations. Therefore, the thermal results obtained from the analysis of the different configurations here proposed can be directly compared to evaluate the effect of the channel shape on the performance of each cooling device.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 6 Conclusions and Future Research Directions
### Decision and Planning Strategies
The following abbreviations are used in this manuscript:  
2D 2 Dimensional 3D 3 Dimensional 3 GPP 3 rd Generation Partnership Project 5G-NR Fifth Generation-New Radio ACC 4 Adaptive Cruise Control ADAF 5 AAB 6 AAB 7 AAB 8 AAB 9 AAB 10 AAB 11 AAB 12 AAB 13 AAB 14 AAB 15 AAB 16 AAB 17 AAB 18 AAB 19 AAB 20 AAB 21 AAB 22 AAB 23 AAB 24 AB 25 AAB 26 AAB 27 AAB 28 AAB 29 AAB 30 AAB 31 AAB 32 AB  
CSMA/CA Carrier-Sense Multiple Access with Collision Avoidance  
C-V2X Cellular Vehicle To Everything  
DA-ANN Deep Auto-encoder Artificial Neural Network  
DBN Deep Belief Networks  
DICE Deep Inference for Covariance Estimation  
DR Dead Reckoning  
DSRC Dedicated Short-Range Communication  
EDCA Enhanced Distribution Channel Access  
eMBB enhanced Mobile BroadBand  
FCTA Forward Cross Traffic Alert  
FCWS Forward Collision Warning System  
FDMA Frequency Division Multiple Access  
FHSS Frequency Hopping Spread Spectrum  
FoV Field of View  
GFSK Gaussian Frequency Shift Keying  
GNSS Global Navigation Satellite System  
GPS Global Positioning System  
HetVNet Heterogeneous Vehicular Network  
ID Identification  
IMU Inertial Measurement Unit  
IoT Internet of Things  
IoV Internet of Vehicles  
IR InfraRed  
ITS Intelligent Transport Systems  
IVC Inter-Vehicle Communication  
LBAC Laser Based Adaptive Cruise  
LCA Lane Change Assistance  
LDPC Low Density Parity Check  
LDWS Lane Departure Warning System  
LiDAR Light Detection and Ranging  
LoS Line of Sight  
LRWPAN Low Rate Wireless Personal Area Network  
LSTM Long Short-Term Memory  
LTE Long-Term Evolution  
LTE-A Long-Term Evolution-Advanced  
MAC Media Access Control  
MEC Multi-access Edge Computing  
MEMS Micro-ElectroMechanical System  
MIVC Multi-hop Inter-Vehicle Communication  
ML Machine Learning  
mMTC massive Machine Type Communication  
mmwave Millimetre Wave  
NFC Near Field Communication  
NHSTA National Highway Traffic Safety Administration  
NLoS Non-Line of Sight  
NOMA Non-Orthogonal Multiple Access  
NS2 Network Simulator 2  
OBU On Board Unit  
OICA International Organisation of Motor Vehicle Manufacturer  
OCB Outside the Context of a Basic service set  
OFDM Orthogonal Frequency Division Multiplexing  
O-QPSK Orthogonal Quadrature Phase Shift Keying  
OMNeT Objective Modular Network Testbed in C++  
PCW Pedestrian Collision Warning  
PDA Personal Digital Assistance  
PSFCH Physical Sidelink Feedback CHannel  
QAM Quadrature Amplitude Modulation
# An Improved Reference Paper Collection System Using Web Scraping with Three Enhancements
## 9 Evaluation and Analysis
### Limitations
#### 9.5.2 Ethical and Legal Regulation  
In developing the _reference paper collection system_, we consider each API's ethics and legal regulations. This system uses publicly available _endpoints_ provided by _Semantic Scholar_, _DOAJ_, and _PubMed_ API. All data retrieval is limited to open-access content and follows each data provider's terms of use and license. The developed system also complies with fair use policies and value restrictions, does not collect personal or sensitive user data, and does not retrieve content that violates copyright or license terms. All information retrieved by the system is used only for academic purposes, thus maintaining ethical integrity in the research process.  
Although the three API platforms do not always explicitly mention the prohibition of scraping in the text of their terms of service, we carefully considered at the key points in the API license agreement. _Semantic Scholar_ stated at [[https://www.semanticscholar.org/product/api/license](https://www.semanticscholar.org/product/api/license)]([https://www.semanticscholar.org/product/api/license](https://www.semanticscholar.org/product/api/license)) [57] to include attribution obtained from \"Semantic Scholar\" on the website or any publication media, regulates rate limits, suspensions, and use restrictions. _DOAJ_ stated at [[https://doaj.org/terms](https://doaj.org/terms)]([https://doaj.org/terms](https://doaj.org/terms)) [58], which explains that it provides article metadata openly and freely for use (CC0) and that the metadata can be accessed through various mechanisms provided (webpage, API, OAI-PMH, or data dump). _PubMed_ also stated at [[https://documentation.uts.nlm.nih.gov/terms-of-service.html](https://documentation.uts.nlm.nih.gov/terms-of-service.html)]([https://documentation.uts.nlm.nih.gov/terms-of-service.html](https://documentation.uts.nlm.nih.gov/terms-of-service.html)) [59], which explains rate limits and facilitates special requests for users who need to request large amounts of data that exceed the request limit.  
Respecting all license agreements on each API, we configured the system the developed system by including user agents in the script code, retrieving primary metadata directly from the API _endpoint_, setting a rate limit of 100 articles per API, and setting a delay time between 1 and 3 s for the request. Regarding incomplete PDF URLs in some metadata, we use web scraping or the fast checking of PDF accessibility as an alternative method.
## 3 Results and Discussion
### GIS Application
#### 3.1.2 Case Scenario 2: Detection of Leakage Locations  
The same procedure applied in Case Scenario 1 is undertaken. The output identified the three locations suspected of leakages: Al Ghaphia, Al Ghuwair, and Maysaloon. Once these locations were identified, a customized interface was run on ArcGIS Pro. The identified leak locations are determined based on the intersections of buffer zones from the low pressure and flow data, as well as areas with low chlorine levels detected using the 'near' function on the software. The GIS approach used in this study is different from other studies on leak detection. For this reason, direct comparisons may not be meaningful. However, other studies using GIS for leak detection found it feasible as a tool for leak detection [32, 33].
## 4 Simulation Results  
Simulations were conducted to evaluate the performance of the proposed scheme for channel nonlinearity compensation. The parameter of the IM/DD channel follows the case shown in [7]. As for the network architecture, the convolution block in \(\mathcal{S}_{1}\) employs only one convolution layer and one pooling layer, in which both the pooling size and stride step were set as 2. The \(\mathcal{S}_{2}\) involves two LSTM layer with the cell size of \((128,256)\). The \(\mathcal{S}_{3}\) employs one dense layer with the neuron size of 50. It is noteworthy that the amount of filters, in terms of \(\mathcal{C}_{l}\) in \(\mathcal{S}_{1}\), should be set equal to the time step \(m\) in \(\mathcal{S}_{2}\) based on empirical trials. In addition, the testing set should be chosen different from that for training.
## 2 Materials and Methods
### Concepts for Discriminating the Oil-Slick Category
#### 2.3.2 Concept 2: Explored Attributes  
The initial exploratory analysis experimentally choose the simpler as possible variables to represent four types of attributes: contextual aspects (33 in number; e.g., latitude, longitude, etc.), SAR scene elements (36; e.g., beam modes and incident angles), size information (10; e.g., area, perimeter, and several ratios using these fundamental morphological characteristics--e.g., perimeter/area, etc.), and SAR basic qualitative-quantitative statistics (423--including all SAR signature forms, e.g., central tendency, dispersion, etc.).  
All size information ratios were identified following the literature but from studies differentiating oil (slicks) from the so-called look-alike features (e.g., low wind zones), rather than categorizing oil (seeps) from oil (spills) as in our analyses. The SAR basic statistics consist of quantities describing the received radar signal strength that are calculated with all pixels of each individual oil slick.  
The knowledge evolution imparted by the initial analysis [29; 30] that experimentally explored a broad set of attributes (i.e., \(>\)500 variables describing each oil slick) led to the first refined study drastically reducing the dimensionality by starting the analyses using only 19 variables that performed with comparable discrimination effectiveness [31; 32]. This reduction in the number of attributes mostly refers to the elimination of: (1) contextual aspects, as their use provides an almost 100% site-specific flawless discrimination that may limit comparisons with other regions; (2) scene elements, as we only use one beam mode and do not consider incident angle variations within a given frame; (3) size information owing the same (or inverted) frequency distributions (i.e., equivalent statistical meanings); and (4) SAR basic statistics with intra-statistical correlation (i.e., highly correlated attributes), which are not suitable to be used in LDAs, because they do not bring useful information to discrimination processes [63].  
The initial exploratory analysis [29; 30] applied a negative value-scaling filter, whereas the first refined study [31; 32] applied a minimum value-scaling filter. These linear scaling operations bring the information of each pixel to the positive domain, as the minimum value-scaling filter is applied to all pixels in oil slicks, in which the new positive value is derived by subtracting the minimum value of each oil slick from the original pixel value.