esa-sceva/satcom-chunk-collection huggingface dataset:
# Enforcing Optimal ACL Policies Using K-Partite Graph in Hybrid SDN
## 4 Proposed Solution
### Decision Tree Traversing for Multiple ACL Policies
After finding the matching node, we find all the interfaces of LE2, i.e., (10.0.4.1, 10.0.4.2, and 10.0.2.2). For destination IP, i.e., 10.0.4.1-10.0.4.2 group G1 is formed, and two rules are implemented at each interface thus, \(ctr_{trans}\), and \(ctr_{ACL}\) values are counted for G1 as \(ctr_{ACL}\) = 4 and \(ctr_{trans}\) = 74 as shown in Figure 13a. Actually, \(ctr_{trans}\) value is calculated from leaf nodes by using a recursive function. Hence we denote the final value. Now we have single interface 10.0.2.2 remaining, group G2 is formed, and two rules are implemented thus, \(ctr_{ACL}\) = 2 and \(ctr_{trans}\) = 74 for this group as shown in Figure 13b. We move to next node LE1, and two rules are implemented at the connecting interface and group G3 is formed with values \(ctr_{ACL}\) = 2 and \(ctr_{trans}\) = 62 as indicated in Figure 13c. There are three other interfaces, i.e., 10.0.1.1, 10.0.1.2, and 10.0.3.1, where interfaces 10.0.1.1 and 10.0.1.2 are connected with source IP, so no rule is implemented on these interfaces. Only single interface 10.0.3.1 is remaining, so two rules are installed, and group G4 is formed, and \(ctr_{ACL}\) = 2 and \(ctr_{trans}\) = 62 for this group and an edge is added to K-partite graph as shown in Figure 13d. Similarly, other interfaces are processed, and groups G5, G6, and G7 are added to the K-partite graph shown in Figure 13d. Next we reach node LE2 that is connected to multiple interfaces, i.e., 10.0.6.1, 10.0.6.2, 10.0.7.1 and 10.0.8.1, where interfaces 10.0.6.1 and 10.0.6.2 are end nodes so a group G8 is formed and two rules are implemented at each interface thus, \(ctr_{trans}\), and \(ctr_{ACL}\) values are counted for G8 as \(ctr_{ACL}\) = 4 and \(ctr_{trans}\) = 2 as shown is Figure 13e. For other interfaces i.e., 10.0.7.1 and 10.0.8.1 an individual group is formed for each interface i.e., G9 and G10 \(ctr_{ACL}\) = 2 for each group while \(ctr_{trans}\) = 4 for G9 and \(ctr_{trans}\) = 32 for G10. It is noted that we have to install ACL policies collectively on all these groups so in K-partite graph these groups are added in series as shown in Figure 13e. ```
1:Input:
2:\(P=P_{1},P_{2},P_{3},...,P_{n}\) is set of network policies
3:DT is the decision tree constructed in Section 4.1
4:G is a adjacency list that contains the groups numbers \(G_{i}\) where i = 0, 1, 2,..., k against \(ctr_{trans}\) and \(ctr_{ACL}\) values where \(ctr_{trans}\) and \(ctr_{ACL}\) indicate the transmissions and rules count. 5:We take a network policy, say Pi <Source IP, Access Policy, Destination IP>. For example, we take P1 = < 10.0.1.1-10.0.1.2, Allowed, 10.0.4.1-10.0.4.2>
6:We take _Destination IP_ of \(P_{i}\). In our example _Destination IP_ of \(P_{1}\), it is 10.0.4.1-10.0.4.2.
# Geovisualization Techniques of Spatial Environmental Data Using Different Visualization Tools
## 3 Results of Visualization
### Static and Interactive Geovisualization of Environmental Data Using QGIS Modules and KML  
We attached an optional OSM (OpenStreetMap) Standard basic map to the visual representation of the vector and raster databases imported into the QGIS environment equipped with unique symbols (Figure 3). After setting the parameters of the modules, we generated two classic third-generation web maps. The classified vector layers show the nitrate and phosphate values according to the Hungarian Standard with graduated symbols in a third-generation web map (Figure 4A,B). As the default setting, the loaded web map shows the classified database which can be freely zoomed in and out, and the length of the specified routes can be calculated. In addition to navigating on the map, we can control the visibility of each layer. Furthermore, there is a Search by location function integrated into the interactive surface which we can use to navigate to the desired geographical location in the web map, thereby informing the user about the spatial distribution of the pollution level. The visibility of the layers can also be controlled by use of the menu located in the right upper corner. Information about the layers can be queried by clicking, and results showing the appropriate data from the attribute table are visible to users in a window and in the interactive query interface.  
Figure 2: Process of geovisualization.  
Figure 4: Interactive web maps from the study area (**A**) Results of geovisualization using QGIS Cloud, (**B**) Results of geovisualization using QGIS gqis2 web.  
Figure 3: Spatial distribution of phosphate and nitrate values in the investigated wells.  
The temporal data to be displayed on the map are represented by creating KML files. The nitrate and phosphate distribution maps of the area from 2013 and 2019 are displayed in the form of overlapping pictures, while the data points are represented as location indicators on the satellite image of Google Maps (Figures 5 and 6). By clicking on the symbol indicating the location of the selected well on the map, we can see the information panel of the given layer, and the HighCharts interactive diagram attached to mash-up site visualizes the distribution of the soil chemistry properties of the wells stored in the databases, thereby allowing the comparison and assessment of nitrate and phosphate values.  
Figure 5: Distribution map of investigated parameters.
## 3 Results
### Conductive Fillers
#### 3.2.1 Carbon-Based Solutions
The experimental results show that the impedance changes by approximately 40% in presence of cells and hence can be correlated with spreading, adhesion and changes in cell density (Figure 2). Thus, in this research, no electrodes were used and instead a smart scaffold was designed, which behaves as a biosensor for evaluating cell behavior (cell adhesion, proliferation) along with directing cellular growth. \begin{table}
\begin{tabular}{c l c c} \hline \hline
**Fillers** & \multicolumn{2}{c}{**Manufacturing Techniques**} & **Conductivity (Scm\({}^{-1}\))** & **References** \\ \hline Carbon black nanofilers & \begin{tabular}{c} Reversible esterification reaction and two \\ crosslinking steps \\ \end{tabular} & / & [77] \\ \hline Carbon fibers and & \begin{tabular}{c} Precipitation, Heat treatment + surface \\ oxidation + PDA coating \\ \end{tabular} & 4 \(\times\) 10\({}^{-4}\)–128, 2 \(\times\) 10\({}^{-2}\) & [83; 84] \\ \hline CNTs & \
\begin{tabular}{c} Electrospinning, chemical vapor deposition \\ and oxidative purification; \\ Pressure-Activated Microsyringe (PAM); \\ High pressure carbon monoxide conversion \\ synthesis and suspension mixing. \\ \end{tabular} & 1 \(\times\) 10\({}^{-9}\)–3.72 \(\times\) 10\({}^{2}\) & [78; 81; 82] \\ \hline Graphene &
\begin{tabular}{c} Electrospinning and hydrazine vapors; \\ co-electrospinning; Electrospinning and 3D \\ ultrasound expansion; Emulsion \\ polymerization by chemical oxidation \\ method; Bioplotter extrusion printing with \\ UV cross-linking \\ \end{tabular} & 0.93 \(\times\) 10\({}^{-7}\)–2.52 & [85; 86; 87; 88; 89; 90; 91] \\ \end{tabular}
\end{table} 
Table 2: Properties of different carbon-based fillers and corresponding techniques used in manufacturing scaffolds.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 2 Related Work  
The main idea of PDR is to detect the steps the user takes while having a good estimate of the length and direction of the step taken by the pedestrian [23]. Using this approach, the accelerometer data are no longer integrated throughout the navigation process. Instead, the data are being used for detecting steps taken by the user, that are then used for position estimation based on the step length of the pedestrian and his orientation. Figure 1 shows an illustrative example of a 2D PDR where the position is only described as east and north components in the LLF.  
PDR systems can be categorized into two categories: strapdown systems and free-moving (handheld devices) systems. For strapdown PDR systems, we utilize the human GC [24; 25; 26]. GC describes the step cyclic motion and is split into two main parts: the stance phase which is the period when the pedestrian foot is in contact with the ground, and the Swing phase which is when the foot is swinging in between two consecutive stances. Human gait analysis studies the human motion patterns through the kinematic, kinetic, spatial, and temporal parameters [25]. The repetitiveness of the GC makes it ideal for breaking down the pedestrian navigation problem, as most body segments have expected cyclic and repetitive accelerations and angular rate signals under nominal motion. A GC example with all phases is presented in Figure 2.  
Figure 1: Pedestrian Dead Reckoning (PDR).  
This approach decreases the accumulated error in position over time as it breaks down the pedestrian navigation problem into smaller independent modules that accumulate less error over time. Alternatively, it imposes new challenges such as optimal step detection, step length estimation, and user orientation tracking. A general block diagram of PDR systems is shown in Figure 3, where the PDR mechanization block combines the information of the detected step--heading and length--and uses them to compute the current position derived from the previous position. This also imposes the requirement of knowing the initial position and attitude as a starting point for the navigation solution.  
Enhancements applied to any of the PDR subsystems will enhance the full system performance, hence the following subsections cover the recent advances in each of the PDR subsystems. Since the aim of this article is to develop a PDR for smartphones, novelties in the subsystem that can benefit free-moving PDR systems are presented.
## 2 Materials and Methods
### Required Sampling Step Size for the Emulation of the Effects of the Electromagnetic Propagation  
Taking into account typical high speed vehicle dynamics and carrier frequencies (around few GHz), even with a simple two-rays model, the maximum expected Doppler shift is usually below few KHz. With reference to Figure 1, a correct emulation of the phenomenon performed by a DSP section requires a sampling step size of tens of microseconds (typically, \(\leq\) 30 us). This is largely guaranteed by the sampling frequencies of the DSP sections that can run up to few GHz. To correctly emulate the multipath phenomenon, it is instead necessary that, during the minimum value of the differential delay of a path, the signal of the generic ray covers a space less than a fraction of the wavelength. The simple two-rays model allows some basic evaluations: for each differential delay \(\tau\), the spectral distance between two contiguous minima of the time-varying transfer function is approximately \(\frac{1}{\tau}\) Hz. If the system bandwidth is \(B\) MHz, and the differential delay \(\tau\) is expressed in \(ns\), the number of in-band minima is therefore about equal to \(\frac{B\tau}{10}\). The bandwidth of each minimum, evaluated at \(-10\) dB compared to the maximum, is equal approximately to \(\Delta f\approx\frac{0.2}{\tau}\) Hz.  
The bandwidth associated to the minima of the transfer function, for some values of \(\tau\), is visible in Figures 5 and 6.  
Assuming a sampling frequency of 2 GHz for the DSP section and a corresponding sampling interval of 0.5 ns, in this interval the \(k\)th path covers about 0.15 m or up to 3 \(\lambda\), for a RF spectrum from 2 to 6 GHz. Considering the phenomenon only in terms of RF propagation, it can be considered sufficient to limit the sampling frequency in order to have at least two samples per period or better.  
Figure 4: Representation of the multipath signal in the complex envelope domain with delays and time-discrete amplitudes without absolute Doppler information.
## 1 Introduction
This paper considers using a solar-powered UAV to secure the wireless communication with an intended node in the presence of eavesdroppers in an urban environment, see Figure 1. The term of securing the wireless communication means that the intended ground node is able to decode the data sent by the UAV while the eavesdroppers cannot. The aforementioned limitations are taken into account in this paper. Specifically, we consider a given urban environment, where the positions and shapes of the buildings are known. Any building is modelled by a prism. Then, the UAV should fly outside the prisms. Additionally, given the positions of the UAV and the ground node, we can easily verify whether they have LoS. Similarly, the LoS between the UAV and the sun can be checked, which makes the estimation of the harvested energy amount accurate. We focus on the UAV path planning problem by taking into account the solar energy harvesting and the instantaneous communication protection. We formulate a multi-objective optimization model to maximize the residual energy of the UAV at the end of the mission, maximize the time period in which the communications between the UAV and the intended node is secure, and minimize the time to arrive at the destination. To address this problem, we propose a Rapidly-exploring Random Tree (RRT) [10] based scheme. This RRT-based scheme captures the nonlinear UAV motion model, and is also computationally efficient considering the randomness nature. From the generated tree, a set of possible paths that end up in a given region, such as the UAV depot (this is to facilitate the UAV collection after the mission, see Figure 1) can be found. For each possible path, we can verify the security of the wireless communication and compute the overall energy consumption, the harvested amount and the time to complete the path. We then pick the path with the maximum value of a joint metric from the feasible paths. The main contributions of this paper are the new problem formulation and the new RRT-based path planning method. We test the effectiveness of the proposed method via computer simulations. By comparing with a benchmark method, the proposed method guarantees the secure wireless communication with the intended node and prevents eavesdropping in a large time window.
## 3 Results and Discussion
### Nanoindentation Measurements  
The results of the hardness measurements and the dependence of the hardness of vanadium oxide thin films on the Ar:O\({}_{2}\) gas ratio during the sputtering process are shown in Figure 13. The hardness gradually decreased with a decrease in the argon content in the sputtering gas ratio from about 2.1 GPa to 0.3 GPa. In the literature, reports on the mechanical properties of vanadium oxides are scarce and determined hardness values vary greatly depending on the method of deposition and microstructure [63; 64]. [PERSON] et al. [63] reported that the hardness of vanadium oxide thin films was equal to 0.2 GPa, which is similar to the values obtained in this work for films deposited with an Ar:O\({}_{2}\) ratio of 2:1. In turn, [PERSON] et al. [64] reported that additional annealing at 300 \({}^{\circ}\)C caused the transition from the amorphous to polycrystalline phase of V\({}_{2}\)O\({}_{5}\) films and a simultaneous increase in hardness from 3.2 GPa to even 6.4 GPa. In the case of this work, the SEM cross-sections, shown as insets in Figure 13b, revealed that the thin films deposited with low oxygen content in the sputtering atmosphere were densely packed and were very smooth. In turn, in the case of the films deposited with high oxygen content, the cross-sections appear more porous and are, therefore, softer.
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 4 Proposed Defense System
### Noise-Adding  
Different from the denoising function, the noise-adding function attempts to perturb adversarial audios so that adversarial attacks would lose or reduce the ability to mislead an SV system.  
The noise-adding function introduces some small noise to the input audio. Noise can take many different forms. For example, if we apply Gaussian noise with a mean of 0 and a standard deviation of \(\sigma\). That is,  
\[A_{2}=A_{1}+\text{Noise} \tag{12}\]  
where \(A_{1}\) is the input audio, \(A_{2}\) is the noise-added audio, and  
\[\text{each element in Noise}\sim N(0,\sigma^{2}). \tag{13}\]  
Figure 5: The mel spectrograms of an adversarial audio, a denoised adversarial audio, and a noise-added adversarial audio. (**a**) Adversarial audio; (**b**) denoised adversarial audio; (**c**) noise-added adversarial audio.  
The algorithm of the noise-adding function is shown in Algorithm 3, by applying the Gaussian noise. If a different type of noise is applied, the probability distribution generator at the line 7 of Algorithm 3 would be different.  
```
1:Input: an audio clip \(A_{1}\), noise variance \(\sigma^{2}\)
2:Output: a noise-added audio clip \(A_{2}\)
3:Require: normal distribution generator \(N\)
4:
5: noise clip \(\leftarrow[0,0,\cdots,0]\), size = length of \(A_{1}\)
6:for each signal noise[i] in noise clip do
7: noise[i] \(\gets N(0,\sigma^{2})\)
8:endfor
9:\(A_{2}\gets A_{1}+\) noise clip
```  
**Algorithm 3** Noise-Adding Function  
We added the Gaussian noise with \(\sigma=0.002\) to the adversarial audio shown in Figure 4(a) and plotted the mel spectrogram of the resulting noise-added adversarial audio in Figure 4(c). Comparing Figure 4(a) with Figure 4(c), we can see that the background blue color of the noise-added audio is slightly lighter than that of the original adversarial audio, indicating that there is more noise in the noise-added audio. Denoising and noise-adding methods are essentially two opposite operations to preprocess input audios: denoising attempts to reduce the noise in an input audio, whereas noise-adding introduces additional noise to it.
## 2 Materials and Methods  
### Data Acquisition  
The research data were derived from the Disaster Risk Reduction Knowledge Service (DRRKS, [[http://drr.ikcest.org/](http://drr.ikcest.org/)]([http://drr.ikcest.org/](http://drr.ikcest.org/)), accessed on 1 March 2022), which was launched in 2016 by UNESCO with a disaster prevention and reduction mission and relies on the International Engineering Science and Technology Knowledge Center (IKCEST) of the Chinese Academy of Engineering, a UNESCO Category II Center. It is a knowledge service designed to provide resources for disaster data to facilitate knowledge discovery [27; 28]. The DRR scholar module of the DRRKS contains various English-language studies related to natural hazards, which are derived from the Web of Science (WOS) database, the world's largest comprehensive academic platform. This study used \"flood\" as the keyword to obtain search results for flood disaster-related studies appearing from 1990 to 2020.  
Python web crawler technology was used to analyze and crawl the literature metadata in the search result list to obtain the URL of each document detail page. Each page was then analyzed to capture the title, authors, journal, address (the author's affiliated institution), publication year, literature type, keywords, abstract, DOI, research field, link, and other fields. The abstract was the main data used in this study. Through screening, 14,076 pieces of literature metadata related to flood disasters were obtained and stored in the SQLite database ([[https://www.sqlite.org/index.html](https://www.sqlite.org/index.html)]([https://www.sqlite.org/index.html](https://www.sqlite.org/index.html)), accessed on 1 March 2022).  
This study explored the distribution law of flood disaster research hotspot areas. It obtained 5551 global flood disaster events occurring from 1900 to 2020 from the EM-DAT database. The flood disaster frequency in each country was calculated and normalized. EM-DAT is an emergency management database maintained by the Centre for Research on the Epidemiology of Disasters (CRED). It records disasters that cause 10 or more deaths, or 100 or more affected individuals, or that trigger a national emergency, or a call for international assistance.
# Properties of Green Tea Waste as Cosmetics Ingredients and Rheology Enhancers
## 3 Result and Discussion
### Zeta Potential for Pretreatment Confirmation  
Zeta potential is related to surface charge density to determine the carboxyl group content in each CNF. As shown in Figure 2, the zeta potential of green tea waste DS 0 is \(-26.6\) mV; green tea waste already has a high negative charge because of glucuronic acid. CNF DS 0.3 and DS 0.4 have zeta potentials were \(-32.9\) mV and \(-38.4\) mV. Meanwhile, CNF of dried hardwood kraft pulp with TEMPO-oxidized addition of 0.7 mM, 1 mM, and 3 mM NaClO were \(-28.6\) mV, \(-38.8\) mV, and \(-51.9\) mV [31]. The addition of 7 mL of NaClO on CNF from dried hardwood bleached kraft pulp has \(-48\) mV [33].  
CNF from bleached kraft hardwood (HW) pulp from Canada with the TEMPO oxidation method and the aqueous counter collision (ACC) method have zeta potential values for original CNF containing 115.2 carboxylate content (ueq g/g) \(-52.81\) mV, homogeneous CNF contain 115.2 carboxylate content (ueq g/g) \(-52.56\) mV, supernatant contains 110.7 carboxylate content (ueq g/g) \(-40.45\) mV, and precipitate contain 110.7 carboxylate content (ueq g/g) \(-43.86\) mV [41]. Zeta potential is responsible for examination of the stability of nanomaterials dispersion in their dispersive media [41; 42]. However, zeta potential cannot be considered a quantitative measure of surface charge density but only a relative assessment of colloid stability [41]. In general, suspensions in water with zeta potential values the same or more than 30 mV were stable [31; 41].
# InoT Sensor Network for Wild-Animal Detection near Roads
## 3 Design of DSN Hardware
### PIR Sensors
The detection distance of the multisegment lens optical system was only 20 m. Even a detection distance of 40 m does not enable the length of the DSN sensitive zone to be increased to 100 m (this is the distance at which roadside-marker posts are now most often built in Lithuania on trunk and country roads), but it should be suitable if DSNs were installed every 50 m.  
On country roads, the distance from the cleared roadside area to the trees, bushes, or tall grass is usually only a few meters, so the designed improved DSN prototypes include three PIR sensors: two aimed at a small angle from the road and the third with a small multisegment lens for monitoring the rear area of the DSN (detection distance up to 5 m). PIR sensors react to a change in thermal radiation, which, in the case of a motion sensor, depends on the temperature difference between the detected object and the environment. If the temperature of the object and the environment are the same, the sensor will not be triggered. The authors photographed animals kept in enclosures with a thermal imaging camera, and it was confirmed that the recorded wavelengths are in a similar range to the PIR sensors.
## 5 Proposed Software-in-Loop System
### Integration with Server Software  
The developed _software-in-loop_ platform takes into account the designed software architecture (Figure 4), which is deeply integrated with the interface module into the server software of the node.  
Analyzing the node's transceiver hardware, there are transmission start/end and reception start/end events, which now have to be simulated. The simulator must keep track of the transmission/reception status of each node to properly simulate their associated hardware. In other words, the simulator has to know what would be the implemented physical modem, which in this work will be CML Microcircuits modem as it is aforementioned. The physical layer bit rate configuration of the system is 1200 bps.  
Communication between simulator and server software is performed through a TCP/IP socket. It is encapsulated in packets which carry the associated commands in their payload. The most important and more used command is the transmission request. There are also other commands to control the parameters of the simulated hardware, such as frequency change and transmission power change, but any of these are used in this work.