esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning
## 2 Related Work
A visualization module was developed to illustrate the network traffic data visually to system users. Although this work seems promising, no experimental evaluation was conducted to assess the proposed system's performance and scalability. Interesting work was presented in [10] for online classification of user activities using machine learning on network traffic. The authors proposed a system for classifying user activities from network traffic using both supervised and unsupervised learning. The proposed method uses users' behavior exhibited over the network to classify their activities within a given time window. An RF model was trained with features extracted from the network and transport layer headers in the traffic flows. The RF model achieved a prediction rate of 97.37% in recognizing the user activities over short temporal windows. However, this system assumes that users are performing a single activity anytime, thereby obtaining one label for each temporal window. This excludes simultaneous activities performed by one user, which is an essential requirement in modern networks. In summary, most of the existing work on statistical [1; 7; 10] and ML-based traffic classification [2; 5; 11] focus on extracting low handcrafted features from the traffic flow, which always depends on the domain experts' experience. These flow features must also be up-to-date to cope with the rapidly changing world of new online applications [3]. Moreover, most of the surveyed DL-based models are designed to work offline, which is not appropriate for real-time network traffic analysis. Furthermore, to the best of our knowledge, none of the current ML-based approaches can be deployed on-site at the network edge with a user-friendly interface, which precludes minimizing the communication delays and enhancing the user experience in using the system.
## 3 Problem Description
### Sensitivity Analysis  
After testing the developed model, a sensitivity analysis determines the efficiency and accuracy of the obtained solution. The aim is to investigate to what extent the changes in the input data influence the results. As part of the sensitivity analysis, 21 scenarios (Sc.) wherein a change was made in the criteria weights were defined. In this case, those with the highest relative weights were chosen, namely C1, C2, and C6. The criterion value is a reduction in each scenario by 15, 30, 45, 60, 75, 90, and 100%. In Sc. 1-7, the weight of criterion C1 was changed, while the values of the other criteria remained unchanged. In Sc. 8-14, the weight of criterion C2 was changed, and in Sc. 15-21, the same was true for C6 (Table 6). In all cases, the ranking of all alternatives remained unchanged (i.e., the same order was maintained as in the main solution). Based on this, it can be concluded that the method is stable enough.  
Figure 2 shows the obtained ranking of the alternatives. From Figure 2, it can be observed that A2 and A3 are lower ranked compared to A1 but better ranked compared to A4.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sc.** & **A1** & **A2** & **A3** & **A4** \\ \hline Sc. 1 & 0.0472 & 0.0207 & 0.0228 & 0.0090 \\ Sc. 2 & 0.0451 & 0.0197 & 0.0215 & 0.0087 \\ Sc. 3 & 0.0429 & 0.0188 & 0.0202 & 0.0084 \\ Sc. 4 & 0.0408 & 0.0178 & 0.0189 & 0.0080 \\ Sc. 5 & 0.0387 & 0.0169 & 0.0176 & 0.0077 \\ Sc. 6 & 0.0365 & 0.0159 & 0.0164 & 0.0074 \\ Sc. 7 & 0.0351 & 0.0153 & 0.0155 & 0.0072 \\ Sc. 8 & 0.0451 & 0.0207 & 0.0229 & 0.0090 \\ Sc. 9 & 0.0448 & 0.0199 & 0.0216 & 0.0087 \\ Sc. 10 & 0.0426 & 0.0190 & 0.0204 & 0.0084 \\ Sc. 11 & 0.0403 & 0.0181 & 0.0192 & 0.0081 \\ Sc. 12 & 0.0381 & 0.0172 & 0.0180 & 0.0078 \\ Sc. 13 & 0.0358 & 0.0164 & 0.0167 & 0.0075 \\ Sc. 14 & 0.0343 & 0.0158 & 0.0159 & 0.0073 \\ Sc. 15 & 0.0481 & 0.0209 & 0.0238 & 0.0091 \\ Sc. 16 & 0.0468 & 0.0203 & 0.0234 & 0.0089 \\ Sc. 17 & 0.0456 & 0.0196 & 0.0231 & 0.0087 \\ Sc. 18 & 0.0433 & 0.0189 & 0.0227 & 0.0084 \\ Sc. 19 & 0.0430 & 0.0182 & 0.0224 & 0.0082 \\ Sc. 20 & 0.0418 & 0.0176 & 0.0220 & 0.0080 \\ Sc. 21 & 0.0409 & 0.0171 & 0.0218 & 0.0079 \\ \hline \end{tabular}
\end{table}
Table 6: Sensitivity analysis of the obtained solution.  
Figure 2: Sensitivity analysis of the obtained solution.
###### Abstract  
The development of alternative environmentally friendly modes of transportation is becoming an increasingly promising solution in traffic-congested and polluted urban areas. E-bikes, as one of them, are recognized as an ecologically sustainable means of transportation that has significant potential to replace motorized modes of transportation that can improve urban mobility. Relying on artificial intelligence and considering an ecological approach when considering the acceptability of e-bikes by setting a direct question for users influences the development of an innovative way of understanding and evaluating the use of more sustainable modes of transportation. In this regard, this study aims to elucidate the main variables influencing the acceptability of e-bike use using artificial neural network (ANN) models--multilayer perceptron (MLP) and radial basis function (RBF). For training and testing the models, data from a random sample obtained through an online questionnaire, which was answered by 626 residents of Belgrade (Serbia), were used. A multilayer perceptron with nine and seven neurons in two hidden layers, respectively, hyperbolic tangent activation function in the hidden layer and identity function in the output layer, gave better results than the radial basis function model. With an accuracy of 89%, a precision of 83%, a recall of 79%, and an area under the receiver operating characteristic (ROC) curve of 0.927, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that the mileage traveled, the frequency of motorcycle use, the respondents' awareness of the pollution in Belgrade, and the age of the respondents have the greatest influence on the acceptability of using e-bikes. In addition to majority acceptability (69.8%), the results obtained by the model can represent a useful basis for decision-makers when defining strategies for the development and application of e-bikes while reducing traffic congestion and environmental pollution in Belgrade.  
artificial neural network; multilayer perceptron; radial basis function; urban mobility; e-bikes +
Footnote †: journal: Applied sciences
## 5 Fault-Free Routing Algorithm of BCCC under the Restricted Connectivity
### Analysis of Fault-Free Unicast Algorithm
Furthermore, for subcases \(|F_{\beta}|\geq k\), \(|F_{\alpha}|<k\), and \(|F_{\beta}|<k\), the time to obtain the path from \(u\) to \(v\) in \(G-F\) is \(O(kn\kappa^{1}(G))\), which is similar to that discussed in the case of \(|F_{\alpha}|\geq k\). In the case of \(\alpha=\beta\), if \(|F_{\alpha}|\leq k\), Algorithm \(\mathit{BCCFTP}\) will firstly obtain a fault-free path from \(u\) to \(v\) in \(\mathit{BCC}_{n,k}^{\alpha}\) by \(\mathrm{PATHSEQ}\)\((u,F,\mathit{BCC}_{n,k}^{\alpha},F_{\alpha})\). The time complexity of this part is \(O(\kappa(G)^{2})\). Then, Algorithm \(\mathrm{BCCFTP}\) will first obtain a fault-free path \(P_{1}\) from \(u\) to a vertex \(s\) and path \(P_{2}\) from \(v\) to a vertex \(t\) in \(\mathit{BCC}_{n,k}^{\alpha}\) in the time of \(O(\kappa(G)^{2})\). If \(P_{1}\) and \(P_{2}\) have a common vertex \(w\), Algorithm \(\mathit{BCCFTP}\) will return \((\mathrm{Path}(P_{1},u,w)\)\(\mathrm{Path}(P_{2}^{-1},w,v))\). Since the time to obtain \(\mathrm{Path}(P_{1},u,w)\) is \(\mathrm{O}(1)\), the time complexity is \(\mathrm{O}(2*\kappa(G)^{2}+O(1))=\mathrm{O}(\kappa(G)^{2})\). If \(P_{1}\) and \(P_{2}\) have no common vertex, the function will run \(BCTP(s,t,G-BCC_{n,k}^{\alpha},F\backslash F_{\alpha})\) to construct a fault-free path from \(s\) to \(t\). The time complexity of this part is \(\mathrm{O}(kn\kappa^{1}(G))\). Therefore, the time to obtain the path from \(u\) to \(v\) in \(G\) is \(\mathrm{O}(2*\kappa(G)^{2}+kn\kappa^{1}(G)+2*1)=\mathrm{O}(kn\kappa^{1}(G))\). In summary, the time complexity of Algorithm \(BCCFTP\) is \(\mathrm{O}(kn\kappa^{1}(G))\).
## 2 Background
### Cold Boot Attacks
These results suggest that the attacker may model the decay in a region as a binary asymmetric channel, i.e., assuming the probability of a 1 flipping to 0 is some fixed number, while the probability of a 0 flipping to a 1 is some other fixed number. Note that an attacker can establish the \"ground state\" of a particular region of memory very easily in an attack by scanning all the bits and counting the number of 0 bits and the number of 1 bits. Furthermore, the attacker may estimate the probabilities by comparing any original content in such region with its respective noisy version. Another major challenge presented to the attacker after obtaining memory images is locating encryption keys in them. This problem has already been addressed in previous research to locate AES keys and RSA keys in a memory image [6], and, although those key-finding algorithms are scheme-specific, their rationale may be easily adopted to design key-finding algorithms for other schemes, since they rely on finding identifying features of the formats for storing a scheme-specific key and using such identifying features as markers to identify sequences of bytes, while searching over decayed memory images. More specifically, the algorithm searches for sequences of bytes with low Hamming distance to these markers and checks that the remaining bytes in a candidate sequence satisfy some conditions. After tackling the previous challenges, the attacker can only aspire to have access to a noisy version of the original key from main memory; therefore, their principal task becomes the mathematical problem of recuperating the original key from a noisy version of the original key, taking into consideration such attacker may have access to reference cryptographic data generated using the secret key, such as cipher-texts for a secret-key encryption scheme, or have access to a public key in the public-key setting. Thus, the main objective in the cold boot attacks setting is design, implement, and evaluate efficient algorithms to obtain the original private key back from its corresponding noisy version for a range of different cryptographic schemes, as well as explore the behaviour of these key-recovery algorithms for various parameters, in particular establish upper bounds of how much noise such algorithms can tolerate.
## 2 Theory  
### Real Axicon  
An axicon can continuously converge the light to different positions along the axis, and it is widely used to produce nondiffracting beams. There are two types of axicons: a positive axicon and a negative axicon, as shown in Figure 1. From the view of geometrical optics, it can be seen that the positive axicon has a convergent effect on the beams, and the negative axicon has a divergent effect on the beams within a certain range of transmission distance.  
The transmittance function of the positive axicon is given by:  
\[t(r)=\left\{\begin{array}{cc}\exp(-ik(n-1)r\tan\theta)&(r\leq D/2)\\ 0&(r>D/2)\end{array}\right. \tag{1}\]  
where \(r=(x^{2}+y^{2})^{1/2}\), \(k=2\pi/\lambda\), \(\theta\) is the refracting angle formed by the conical surface with the flat surface, \(n\) is the refractive index, and \(D\) is the diameter of the axicon.  
In general, the incident beam is a Gaussian beam, in which case the resulting beam after the axicon is called Gaussian-Bessel beam, and its intensity profile is given by [17]:  
\[I(r,z)=2\pi k\big{(}\tan^{2}\theta\big{)}(n-1)^{2}zI_{0}e^{-2((n-1)\tan\theta /\omega_{0})^{2}}J_{0}{}^{2}(k(n-1)r\tan\theta) \tag{2}\]  
Figure 1: The principle of a real axicon: (**a**) positive axicon, (**b**) negative axicon.  
where \(r\) and \(z\) are the radial and longitudinal coordinates respectively, \(I_{0}\) is the incident on-axis intensity, \(J_{0}\) is the zero-order of Bessel function, and \(\omega_{0}\) is the waist of the incident beam.  
By finding the first zero of the Bessel function, the width of the central lobe \(r_{0}\) and the maximum transmission distance \(z_{\text{P-max}}\) of nondiffracting beams are given by the following equations:  
\[r_{0}=\frac{2.4048}{k(n-1)\tan\theta} \tag{3}\]  
\[z_{\text{P-max}}=\frac{r_{\text{P}}}{(n-1)\tan\theta} \tag{4}\]  
where \(r_{\text{P}}\) is the radius of the positive axicon, and the subscript \(p\) is represented of the positive axicon, as shown in Figure 1a.  
When the parallel beam illuminates the negative axicon, the light is diverged and transformed into an annular beam, as show in Figure 1b, where the subscript \(n\) is represented of the negative axicon. From Figure 1b, the inner radius \(r_{0}\) and outer radius \(R_{0}\) can be obtained as:  
\[r_{0}=z_{\text{n}}\tan\varphi \tag{5}\]  
\[R_{0}=(z_{\text{n}}-L_{\text{n}})\tan\varphi+D/2 \tag{6}\]  
where \(\varphi=arcsin[(n-1)\theta_{n}]\), \(L_{n}\) is the thickness of the axicon and \(\theta_{n}\) is the conical angle. Then the width of the output beams from the negative axicon is:  
\[R_{0}-r_{0}=D/2-L_{\text{n}}\tan\varphi\approx D/2-L_{\text{n}}/\varphi=D/2-L_ {\text{n}}(n-1)\theta_{\text{n}} \tag{7}\]  
From Equation (7), we can see that the width remains constant within the transmission distance \(z_{\text{n}}\), and it is just related to the radius of the axicon \(D\), depth \(L_{\text{n}}\) and diverge angle \(\theta_{\text{n}}\).
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 8 Simulation
### Simulation Results
#### 8.3.2 Effect of Beacon Sending Period  
The UAV starts its data collection process by sending beacon messages to activate the sensor nodes. This beaconing period decides the size of clusters, and the longer the beaconing period the larger the cluster size. This phenomenon can also affect the performance of the system. The developed system is tested against varying beacon sending periods and the results are shown hereinafter.  
Figure 9(b) represents the total number of dead nodes on the X-axis, simulation time on the Y-axis and different curves show the performance of system using different beacon periods. The lower the curve the better is the performance. It is observed in Figure 9(c) that by increasing the beaconing period the clusters become larger in size and fewer in number. Number and size of clusters may also effect the energy utilization of the system, which is further investigated in Figure 11.  
Figure 11: Effect of beaconing period on energy utilization (**a**) 2 s beaconing (**b**) 4 s becoming.  
Figure 10: Simulation results of different cases (**a**) Number of dead nodes vs. simulation time (**b**) effect of varying beacon sending period.  
Figure 11a shows the remaining energy of each node at the end of simulation while using 2 s beacons. More high peaks mean more nodes are unutilized. Figure 11b shows similar results as Figure 11a but we considered have 4 s beacons.  
The system is evaluated by changing the beaconing period from 1 to 4 s. If a 4 s beacon is used about 580 clusters are made in 100 rounds. Further, in the case of a1 s beacon the number of clusters is 980. Figure 11a shows that about 10 nodes are left unattended or unutilized at the end of the simulation, while in Figure 11b the energy utilization is improved in the sense that only two nodes are observed unutilized and few are underutilized.
## 1 Introduction
They achieved improved graphite dispersions by adding the non-ionic dispersant polyvinylpyrrolidone (PVP). The particle size distribution that they obtained was \(<\)1 \(\mathrm{\SIUnitSymbolMicro m}\) for particle flakes with a diameter of \(15\,\mathrm{nm}\), although the PVP content was not indicated. [PERSON] et al. [27] demonstrated the suitability of poly(acrylic acid) (PAA-NH) in increasing the dispersion of natural graphite in an aqueous medium in conventional LIB production. The scope of their investigation did not include an analysis of the particle size distribution. [PERSON] et al. [28] examined six different CB powders in aqueous solutions and achieved stabilization while using a non-ionic dispersant, Triton X-100 (TX-100). They investigated the adsorption on the basis of isotherms rather than the particle size distribution. [PERSON] et al. [29] examined the suitability of dispersants in stabilizing CB in an aqueous lithium iron phosphate (LFP) dispersion, focusing on dispersion time, equipment, and dispersant content. The dispersants investigated comprised a cationic type, hexadecyltrimethylammonium bromide (CTAB), a non-ionic type, sodium dodecyl sulphate (SDS), and TX-100. The most homogeneous distribution was achieved with TX-100, although the particle size distribution displayed a distinct fraction within the range of \(1\,\mathrm{\SIUnitSymbolMicro m}\) and \(10\,\mathrm{\SIUnitSymbolMicro m}\). [PERSON] et al. [30] examined the effect of PVP on graphite powders with a mean diameter of \(100\,\mathrm{nm}\) as well as on CB powders with a mean diameter of \(24\,\mathrm{nm}\), respectively. Particle size distributions with large fractions above \(1\,\mathrm{\SIUnitSymbolMicro m}\) were obtained with both materials. While most of the previous studies concentrated on examining the impact of dispersants in conventional electrode dispersions or in applications outside the field of battery technology, this paper primarily aims at investigating their suitability in printing electrodes for LIBs. The focus is on graphite and CB materials that are suitable for LIB production. In addition to examining the particle size distribution, the application of dispersants is also evaluated with regard to the expected final cell properties. The paper concentrates on the dispersion behavior of organic electrode components, rather than ionic components, such as active materials in cathode dispersions. A systematic approach is developed to further generalize the findings of this study, which is based on a more profound detailing of the prevailing stabilizing mechanisms.
## 2 Materials and Methods
### Automatic Irrigation System  
An automatic irrigation system with the soil moisture information controlled the irrigation according to the soil moisture changes and irrigation threshold criteria in the field. It consisted of a sensor unit, a control unit, and a driving unit. The sensor unit measured the soil information in real time using an FDR sensor. The control unit was composed of a control panel that directed the device by calculating the set irrigation value using the soil moisture information measured by the sensor unit and an application set directly by remotely operating the control panel. Finally, the driving unit operated the electronic pump and solenoid valve to supply water to the soil.  
Figure 2: The algorithm for intermittent irrigation.  
The volumetric water content was the ratio of the volume of water to the unit volume of soil. There are various sensors for measuring the volumetric water content [19]. In this study, the volumetric water content was measured in real time using an FDR sensor that transmitted an electrical signal to the soil and measured the response through a frequency analysis using the difference in the permittivity of the soil and water. The FDR sensor (WT1000B, Mirae Sensor Co., Ltd., Seoul, Korea) used in this study had the ability to measure the volumetric water content, soil electrical conductivity (EC), and soil temperature. Figure 3 shows the FDR sensor and Table 1 shows the specifications of the FDR sensor.  
Pressure pumps were used to supply water at an appropriate pressure and solenoid valves were used to regulate the flow of water by a remote control. Figure 4 is a photo of the pumps and valves that were set up in the field. Using the soil moisture information measured by the FDR sensor, the control panel operated the pressure pump and solenoid valve to irrigate the soil. In addition, users could directly remotely operate the control panel through a mobile application on an Android OS. Figure 5 shows the control panel location during the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline
**Measuring Range** & **Accuracy** & **Operating Temperature** \\ \hline Moisture: 0–99.9\% & Moisture: \(\pm\)1\% & \\ Soil EC: 0–6.0 dS/m & Soil EC: \(\pm\)0.1 dS/m & 0 \({}^{\circ}\)C–60 \({}^{\circ}\)C \\ Temperature: 0 \({}^{\circ}\)C–60 \({}^{\circ}\)C & Temperature: \(\pm\)0.5 \({}^{\circ}\)C & \\ \hline \end{tabular}
\end{table}
Table 1: The specification of the FDR sensor.  
Figure 3: The photos of the FDR sensor.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 1 Introduction
Recent developments (presented later in Section 9) using Cerenkov light converted in solid photocathodes have shown the capability of reaching a time resolution of 24 ps for Minimum Ionizing Particles (MIPs). Figure 1: When a particle passes through the Micromegas detector, it ionises several atoms in the conversion volume, and the primary electrons drift to the amplification region. In the amplification region, an electron avalanche is formed. * An energy resolution of \(\sim\)11% (Full Width at Half Maximum) (FWHM) with 5.9 keV photons [7], which is a result close to the limit imposed by statistical fluctuations, showing that the intrinsic fluctuations during the amplifications are small.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 2 Related Works
### Matchability and Uncertainty  
Some works train the occlusion prediction subnetwork using occlusion labels as explicit supervision [28]. While this approach easily models occluded pixels, it still fails to model mismatched pixels due to abnormal exposures, texturelessness, etc., and is difficult to train on sparse stereo datasets such as KITTI [23; 24], which lack occlusion labels. Differently, the training of the proposed MUIR does not rely on occlusion labels. The predicted matchability mask models most of the mismatched pixels and is a by-product of the supervised training using only disparity ground truth.  
Confidence or uncertainty is used to assess disparity reliability in learning-based methods. In confidence-based methods, the disparity confidence is predicted with patchwise [34; 35; 36] or image-wise [37; 38] input to a CNN-based subnetwork trained with binary cross-entropy loss (BCE loss). Unlike confidence \(\in[0,1]\), uncertainty \(\in[0,+\infty]\), based on [17], is used in multiple regression tasks [39; 40] to model the error in the predicted values. In [41], uncertainty-guided refinement is performed after cost aggregation to improve disparity accuracy. The CVA-Net by [42] uses matching cost volume to predict aleatoric uncertainty. It is then combined with modified GC-Net [20; 43] to improve disparity accuracy. Ref. [44] exploits the intermediate multi-scale disparity maps from [5] to predict the disparity uncertainty. In addition, the KL divergence loss is proposed to match the distribution of uncertainty with disparity error, thus improving the uncertainty accuracy. Differently, the proposed MUIR prioritizes the accuracy of disparity over uncertainty. It aims to improve the robustness by enabling the model to become aware of the uncertainty. Furthermore, the disparity uncertainty of each iteration is predictable, which ensures the scaling flexibility of the model.