esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 4 Performance Evaluation
### Impact of Different Node Density on Routing Performance  
In this section, we compare the RSNA routing scheme with other routing schemes under different node density. The results and analysis are shown as follows.  
Figure 4a shows that the delivery ratio of all schemes improves as the node density increases accordingly, because more nodes implies more chances of forwarding, and thus higher delivery ratio. Due to the whole network flooding strategy with redundant replications, Epidemic has the lowest packet delivery ratio. The partial flooding of SprayAndWait and the relay selection mechanism of Prophet can effectively reduce the number of generated packets and achieve a higher packet delivery ratio than Epidemic's blind forwarding. However, they are not taken into consideration the delivery competency of further intermediate nodes and forwarding willingness based on device capacity and node behaviors, so the delivery ratio improves more slowly than RSNA.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Combination \(\sharp\)** & \(\alpha\) (\(\mu\)) & \(\beta\) (\(\lambda\)) \\ \hline
1 & 0.1 & 0.9 \\
2 & 0.2 & 0.8 \\
3 & 0.3 & 0.7 \\
4 & 0.4 & 0.6 \\
5 & 0.5 & 0.5 \\
6 & 0.6 & 0.4 \\
7 & 0.7 & 0.3 \\
8 & 0.8 & 0.2 \\
9 & 0.9 & 0.1 \\
10 & 1.0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RSNA parameters.  
Figure 3: (**a**) Delivery ratio vs. 10 coefficient combinations. (**b**) Overhead ratio vs. 10 coefficient combinations.  
From Figure 4b, we can see that Prophet and SprayAndWait have a long average latency, because message carriers have to wait for an appropriate intermediate node for forwarding messages. Because the Epidemic scheme is based on flooding forwarding strategy without the relay node selection, it has a slightly higher latency than RSNA. The proposed RSNA scheme reduces the number of aborted messages due to the prediction of insufficient relay nodes and evaluation of relay possibility. Therefore, it has the lowest average latency.  
Figure 4c shows the comparison of overhead ratio. As expected, Epidemic has the highest overhead, because the carrier of the same message will increase as the node density improves. In Prophet, it has more opportunities to choose an appropriate relay of higher delivery probability when the number of nodes increases, and thus has a large overhead ratio. Each message has a fixed cope in SprayAndWait, so the overhead is stable when the node density increases. However, the carrier of RSNA can select an efficient forwarder via the utility metric based on node attributes during message relaying, so RSNA has a lower overhead ratio than the other three algorithms.
## 4 Discussion
### Acoustic Parameters
#### 4.1.1 Reverberation Time  
The most striking result to emerge from the data is that of the small differences for RT measurements of the proposed method as compared with measurements with a dodecahedron speaker. Even at low frequency range, where other low-cost methods usually do not accurately predict the RT, the proposed method seems to provide excellent results.  
The RT is evaluated from the slope of the integrated impulse response curves according to ISO 3382-1:2009. For all measurements (Figures 6 and 8, Tables 1 and 2) with the Behringer speaker, the mean absolute error compared with measurements with a dodecahedron speaker was less than 0.06 s if the 12 positions method was utilized. Greater mean absolute error (0.08 s) was found for the Genelec speaker. Also, the relative error of the RT with respect to the corresponding JND as presented in Table 1 is below 1.5 JND units for all spaces. Therefore, in acoustic measurements, similar results can be expected if a two-way full frequency range common directional loudspeaker is utilized. However it is worth mentioning that RT is the most stable parameter in acoustic measurements [46].  
The Philips speaker did not provide similar results especially in the low frequency range (Figure 13) probably because of the low signal to noise ratio. Hence, one-way design multimedia speakers should be avoided for acoustic measurements with the proposed method.
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 4 Experimental Results
### Complexity Analysis  
We analyzed the complexity of EDC-Net based on two criteria: (1) time consumption for point classification; (2) model size. The results are summarized in Table 3.  
For all the experiments performed by EDC-Net, the average running time was about 0.3 s for point classification per point cloud. In comparison, the average running times for point classification by EC-Net [21] and PIE-NET [17] were 0.8 and 0.5 s respectively.  
The size of the EDC-Net model is significantly smaller than the size of the EC-Net [21] model, and it achieved better performance; this is a notable advantage of EDC-Net that determined it as a light-weighted model and proved its capability to fit into devices with limited resources.
## 3 Data Visualization and Analysis Tool
### Automatic Line Identification  
The emission lines of a recorded spectrum can also be identified automatically by a method that was developed in house [32]. The method is a type of spectral unmixing that involves solving a system of linear equations between an intensity calibrated measured LIBS spectrum and a superposition of reference spectra. In our approach, the reference spectra consist of computationally simulated atomic emissions from typical rock-forming and trace elements and combinations of plasma temperatures, electron densities, and atomic densities. The simulations are obtained from a homogeneous and stationary one-dimensional plasma model under the assumption of local thermal equilibrium [39]. For the simulated emission spectra, temperatures ranging from 5000 K to 30,000 K in eleven equidistant steps are used. We consider electron densities between \(1\times 10^{22}\) m\({}^{-3}\) and \(2\times 10^{24}\) m\({}^{-3}\) with six equidistant steps on a logarithmic scale and atomic densities between \(1\times 10^{20}\) m\({}^{-3}\) and \(1\times 10^{24}\) m\({}^{-3}\) in five equidistant steps on a logarithmic scale. This range allows us to approximate time-integrated input spectra by using a superposition of simulated reference spectra with different plasma properties. By solving the system of equations, we obtain coefficients for each reference spectrum that was used to match the input spectrum. To obtain a score for each element, the coefficients for the normalized reference spectra corresponding to a given element are summed. The computed score is thereby a measure of how much of the input spectrum signal can be attributed to a given element. It should be noted that this score does not reflect a quantitative measurement of the sample's constituents.  
When applying the automatic line identification tool in the analysis application, an element threshold score can be set inside the corresponding dialog and all elements that fall above that threshold are selected automatically in the Line Browser. Furthermore, the synthesized spectra for each element can be displayed, as well as the fitted sum spectrum. The result of running the automatic spectrum analysis is shown in Figure 6b.
## 3 Results and Discussion
### Beam Based on the Topology Optimization
Figure 10: Maintained mass vs. maximum displacement for experiment OT1. The objective of experiment OT3 was to minimize the mass using _FOS_ as the variable (_FOS_ values from 1 to 3 were tested, at 0.25 increments). In Figure 12 some optimized solutions depending on _FOS_ are shown. One of the main findings obtained from experiment OT3 was the relationship between fractal dimension and _FOS_, which is depicted in Figure 13. The fractal dimension exhibited a positive relationship with _FOS_, which was linear and could be fit with an \(R^{2}>0.94\) (see the filled circles and continuous line in Figure 13). Analogously, the maintained mass followed a positive linear relationship with _FOS_ (see empty circles and dotted line in Figure 13). When the objective was to minimize the mass, higher _FOS_ values were obtained with higher percentages of maintained mass, which is in accordance with the lightened solutions with higher fractal dimension. Therefore, in experiment OT3, the fractal dimension increased simply because the optimized beams had more mass. The second main result of experiment OT3 was the inverse relationship between the maximum displacement and the fractal dimension (Figure 14). This decreasing relationship could be adjusted to a polynomial of degree 2, with a goodness of fit \(R^{2}>0.97\). In this case, the relationship did not show an asymptotic region as it did in OT1. The fractal dimension increased along with the maintained mass, and thus the fractal dimension cannot be used as a parameter for assessing the geometric efficiency of the solutions when the objective is to minimize the mass. In experiment OT4, the objective was to minimize the mass using the thickness as a variable. Thickness was evaluated with values from 200 to 500 mm, with increments of 100 mm. As in OT2, the results indicate that the fractal dimension is not related to mechanical resistance parameters when the thickness of the optimized beams changes. Figure 12: Representation of beams for experiment OT3 with an _FOS_ equal to (**a**) 1, (**b**) 1.25, (**c**) 1.75 and (**d**) 2. Figure 13: _FOS_ vs. fractal dimension for experiment OT3.
# An Efficient Framework for Securing the Smart City Communication Networks
###### Abstract
In turn, this will include collecting data and conducting analysis of a secure control network in smart city communication, comparing and determining secure and insecure traffic parameters using authentication and key session schemes, and eventually evaluating the performance of a secure control network in smart city communication using authentication and key session schemes. In this paper, we provide a new framework/scheme for enhancing secure control in smart city communication networks by boosting security performance. This works by enforcing data secrecy and authentication using efficient key exchange mechanisms. In order to secure a two-way communication channel, a smart city system can achieve mutual authentication and build shared session key schemes between smart meters and the control center. The remainder of this paper is organised as follows. In Section 2, we review the related work. Section 3 describes our proposed cyber security framework for smart cities in detail. Section 4 presents and discusses the results of our simulation study. Finally, Section 5 concludes the paper.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism  
Unlike RPL and other existing routing protocols, the proposed mechanism addresses various parameters associated with routing and energy optimization in the network. A node scoring mechanism is introduced based on the nodes' existence in the network. The neighborhood of a node is further classified as closed or identical neighbors. Some of the procedures in this study were influenced by our previous work on reward-based mechanisms (RwBMs) [9]. The RwBM is a game-based approach that uses the Rubinstein bargaining game for the management of virtual currencies which are referred to as scores. Our previous work deals with the management of selfish nodes in WSNs using the RwBM mechanism. In this study, we proposed a novel scoring scheme to select the best relay nodes while choosing a path. Herein, the key algorithm for the score manipulation and calculation, which involves entirely different procedures, is distinguishable from the RwBM.  
This section is divided into two major subsections. In Section 3.1, the preliminaries are discussed, and in Section 3.2, the entire mechanism is explained in detail.
# Outlier Reconstruction of NDVI for Vegetation-Cover Dynamic Analyses
## 2 Materials and Methods
### Methods
#### 2.2.2 Tensor Construction  
A tensor is a multidimensional array, and the number of dimensions is called the order of the tensor, also named as ways and modes [40]. In tensor terminology, the order of a tensor \(\mathcal{X}\in\mathbb{R}^{I_{1}\times I_{2}\times\ldots\times I_{N}}\) is \(N\), and the elements of \(\mathcal{X}\) are denoted as \(a_{i_{1}i_{2}\ldots i_{N}}\). To model vegetation-cover conditions in different years and the same geographical area under the same spatial resolution, a tensor \(\mathcal{X}\in\mathbb{R}^{I\times I\times K}\) can be constructed on the basis of NDVI pixels, with the three dimensions standing for row number, column number, and acquired date of pixel, respectively, as shown in Figure 2a. An entry \(\mathcal{X}(i,j,k)=c\) denotes that the value of pixel in the \(i\)-th row, \(j\)-th column and in the \(k\)-th year is \(c\).  
In tensor terminology, an \(n\)-order tensor can be formulated as a matrix by unfolding, and the elements of the tensor are mapped to the matrix.  
\[unfold_{n}(\mathcal{X}):=\mathbf{X}_{(n)}\in\mathbb{R}^{I_{n}\times(I_{1} \ldots I_{N})}, \tag{2}\]  
and the elements of the tensor are mapped to matrix the element\((i_{n},j)\), where  
Figure 2: Tensor and sliding-window-based tensor stream analysis (The process of tensor stream construction and tensor decomposition are presented in (**a**), and (**b**) shows the process of reconstructing outlier based on SWTSA. \(\mathcal{X}_{d}\), \(\mathbf{W}(d,w)\) and \(\mathbf{U}^{(i)}\) represent the tensor, sliding window and the factor matrix, respectively).  
\[j=1+\sum_{\begin{subarray}{c}k=1\\ k\
eq n\end{subarray}}^{N}{(i_{k}-1)}J_{k},\ \ \text{with}\ \ J_{k}=\prod_{ \begin{subarray}{c}N-1\\ N\
eq n\end{subarray}}^{k-1}{I_{N}}, \tag{3}\]  
The \(n\)-mode product of a tensor \(\mathcal{X}\in\mathbb{R}^{I_{1}\times I_{2}\times\ldots\times I_{N}}\) with a matrix \(\mathbf{U}\in\mathbb{R}^{I_{n}\times I_{N}}\), denoted as \(\mathcal{X}\times_{n}\mathbf{U}\), is given by  
\[\left(\mathcal{X}\times_{n}\mathbf{U}\right)_{i_{1}\ldots i_{n-1}i_{n+1}\ldots i _{N}}=\sum_{i_{n}=1}^{I_{m}}{x_{i_{1}i_{2}\ldots i_{N}}u_{j_{n}}}, \tag{4}\]  
In general, the size of tensor is represented by a norm, and the Forbenius norm of a tensor \(\mathcal{X}\in\mathbb{R}^{I\times I\times K}\) is denoted by [40; 41]  
\[\left|\left|\mathcal{X}\right|\right|_{\text{F}}=\sqrt{\sum_{i=1}^{I}\sum_{j= 1}^{J}\sum_{k=1}^{K}{x_{ijk}^{2}}}, \tag{5}\]
## 3 Path Loss Models
### UMa Path Loss Models
#### 3.2.1 5 GCM Model  
CI and ABG large-scale path-loss models are used to measure the PL in an urban macro environment [14; 36; 42]. In a UMa environment, the BS antenna height is 25 m, which is higher than the UMi case. This will help reduce the PL by avoiding obstructions below 25 m height from the ground surface [19].
## 1 Introduction
[14] obtained high extraction efficiencies in the elimination of phenolic compounds from water using [OMim][BF4], even after six cycles. It is also possible to apply these solvents to remove phenols from other matrixes; for instance, [PERSON] et al. [15] used imidazolium-based ILs with different anions (Cl-, Br-, BF4- and PF6-) to remove phenol from a model oil. The results showed that phenol can be removed with high extraction efficiency, obtaining the best result with [BMim]Cl, and ILs can be recovered by evaporation and reused four times without loss of efficiency. In this work, the capability of four ILs, 1-propyl-1-methylpiperidinium bis(trifluoromethylsulfonyl)imide [1P1 Mpip][NTf2], 1-propyl-3-methylpyrididinium bis(trifluoromethylsulfonyl)imide[1P3 Mpy] [NTf2], 1-propyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide [1P1 Myr][NTf2], and 1-propyl-1-methylpyrrolidinium bis(fluorosulfonyl)imide[1P1 Myr][NTf2] to remove phenol, o-cresol, 2-chlorophenol, and a mixture of these three phenolic compounds from water was studied. The structures of these compounds are presented in Table S1. The final concentration of phenolic compounds in the aqueous phase was measured using the 4-amineantipyrine method by UV-Vis spectrophotometric technique, and the extraction efficiencies were calculated [16]. The effect of the structure of the ILs and the structure and initial concentration of the phenolic compounds in the extraction efficiencies will be discussed in the work. As a result of the high extraction capacity of these solvents to remove phenolic compounds from water, a real wastewater was simulated, preparing an aqueous mixture of these three phenols, in order to analyze their capability to remove these pollutant substances when they are present together, as it happens in a real procedure.
## 2 Literature Review
### Networked Color Sensors
#### 2.5.3 Color Correction  
The next step after color cast detection is color correction. Color correction describes object intrinsic color under different lighting conditions, and applies to medical images, remote sensing images, mural images, licenses and many others. Some classic methods for color correction are grey world color correction [37] and perfect reflection color correction [38].  
1. Grey world color correction assumes that in a normal well color balanced image, the average of all colors is a neutral grey. We can calculate the illuminant color by comparing the average floor with neutral grey, but we cannot use this method in images with a single dominant color.  
Perfect reflection color correction. The object itself has no color; it shows color through a different wavelength of light absorption, reflection and projection. If the object is white, it reflects all light. The white object or area is the perfect reflector. Perfect reflection theory assumes that the perfect reflector is standard white in the image. No matter which light, a white object, the R, G, and B of its image are great values. Based on the perfect reflector, they correct other colors.  
The two-color correction methods are suitable for most color corrections. The calculations are simple, but sometimes cannot restore the real object color. We will use our proposed method to verify the feasibility of the protocol using color sensors.
## 4 Medical Information System Development
### Automatic Classification Module  
Models for classification have been created in GT for detecting skin pathologies (melanoma, basal cell carcinoma, actinic keratosis, and seborrheic keratosis) and for detecting ocular pathologies (glaucoma, cataract, diabetic retinopathy). Subsequently, the models were uploaded to the Google Teachable cloud. Later, the JavaScript source code will be used to embed it in the integration with the SF platform. To call the model in SF, VFP (Visualforce Page) was used. The code arguments are:  
* Model (the file containing the model's topology (json)).
* Weights (the file containing the weights).
* Metadata (the file containing the model's labels and other additional information) [38].  
**i. Image**  
Within VFP, the front end of the application was implemented, and GT (Google Teachable) libraries were attached to access the classification model. Specifically, HTML code for loading an image, for the background of the classification module, and the button triggering the JavaScript controller with integration to Google Teachable can be found. The JavaScript custom controller is kept in a static resource and is called at the VFP level, thus maintaining the reuse characteristic and easier management of the controller. The button calls a \"initPicture()\" method at the controller level, which retrieves the model, metadata from GT and checks the predictions from the classifier. At the same time, an error message is returned if any type of error occurs while reading the image. Integration was achieved by accessing public URLs from GT.  
**ii. Video**  
For the video solution, the same working method was implemented as in the image loading solution. The difference lies in the use of the computer's webcam. Specifically, when the button is pressed, the camera opens, utilizing the model and metadata from GT to run the classifier in real time and make predictions for the acquired video images. This classification module displays the result in real time with the acquisition of video images.
## 4 Application to Data
### Testing the Methodology on a Reference Pulse: The 92238 Case
The Be line diagnostic returns an average (median) triggering time of 250 \(\upmu\)s (175 \(\upmu\)s) with a standard deviation of 37 \(\upmu\)s, while the UV diagnostics returns an average (median) of 280 \(\upmu\)s (250 \(\upmu\)s) with a standard deviation of 18 \(\upmu\)s.  
The results of the two analyses are mostly in accordance, and the average value is comparable with the JOREK numerical simulations reported in literature [26], where the authors found a triggering time (from the pellet injection to the ELM triggering) of about 270 \(\upmu\)s.  
It is worth discussing the sources of uncertainty that may affect the results. The first limit on the time resolution is the sampling rate of the diagnostics: the beryllium has a sampling rate of 10 kHz (\(\Delta t_{Be}=100\)\(\upmu\)s), the diamond UV of 200 kHz (\(\Delta t_{UV}=5\)\(\upmu\)s), and the pellet signal monitor is at 1 MHz (\(\Delta t_{Pellet}=1\)\(\upmu\)s). Therefore, the time resolution limit due to the diagnostic sampling rate is about 100 \(\upmu\)s for the beryllium-based analysis and 6 \(\upmu\)s for the diamond-UV analysis. Moreover, a decrease in the time resolution is due to the spectrogram calculation, which is performed in a specific time window (see Section 2.3). For the sake of completeness, the reference assumptions of time delay in the present analysis also have to be clarified. In the case of Be spectroscopy, the signal is often characterised by two maxima, one very close to the rising edge, and a second one situated from 1 ms to 2 ms after it, as shown in Figure 11a. The second peak is also usually higher than the first. The edge-peak distance also proves not to be constant. Its distribution spans from a delay of 0.8 ms to 2.4 ms, involving that an analysis based on peak-to-peak distance would be affected by significant uncertainty on the triggering time that would vary from about 1  
Figure 10: Average S PCC profile of natural and triggered ELMs for shot 92238 using beryllium line and diamond UV diagnostics. ms to 3 ms [18]. Instead, the rising edges of the signals have been considered to perform all the statistics since they are not affected by this type of uncertainty.