esa-sceva/satcom-chunk-collection huggingface dataset:
# Future of IoT Networks: A Survey
## Future IoT Network
### Management of IoT Devices on IoT Network  
For IoT devices to access the IoT network, the IoT network itself should support a mechanism of plug-and-play for the IoT devices [80]. Current devices are primarily controlled by users in terms of turning on the device and connecting the device to the network. However, the IoT device should not be configured manually by the users and should be automatically configured. To support this feature, the plug-and-play mechanism is crucial for the IoT network because it can automatically connect the IoT devices to the IoT network. For example, the authors in [81] proposed plug-and-work to orchestrate numerous devices to automatically connect within industrial and production systems. Plug-and-work concentrates on self-configuration mechanisms by enabling a secure plug-and-work environment for IoT. Nonetheless, the IoT network should be able to provide a connectivity mechanism for connecting trillions of devices and managing networking addresses regardless of the type of network connection.  
Furthermore, there are issues with assigning IP addresses to IoT devices and data. To efficiently manage IoT devices, an efficient and scalable addressing scheme for connecting IoT devices to the Internet is needed. One of the solutions to handle the addressing issue in the IoT network is to employ the IPv6 address allocation scheme [82; 83]. The IPv6 addressing architecture defines two scopes for a unicast address, link-local and global. The link-local address is used for auto-discovery and auto-configuration. It is used for the local network and does not guarantee uniqueness in a larger network. Moreover, it will not be forwarded by the routers to the other links. The global scope address is expected to be used as a globally unique address. Thus, the device can utilize a global IP address to communicate over the Internet and utilize the link-local address to connect to the local area network. Specifically, the Unique Local Address (ULA) is designed for local networks larger than a single link but not for communication with the Internet [84]. Globally unique addresses (GUA) are administered to provide a unique and routable address for the Internet communication. For devices that do not require Internet communication, the address schemes can be employed as a ULA. However, in case the IoT devices need to communicate in the global network, it can employ the GUA address. Consequently, the IPv6 will not only ease the issue of scalability of the networking address, but it can also be configured in an adaptive manner to increase the efficiency of the IoT network [85]. Further, with the IPv6 technologies to maintain and allocate IP addresses for IoT devices, the IoT network can handle connectivity management from a local network to a global network.
## 3 Modeling Framework
### Ground Structure Method
#### 3.1.1 Discrete Strategy  
In this strategy, an amount of discrete points among the design domain is needed to contain the structural data after the topology evaluation step under the GS method, as the GS is a polygon mesh-based method, which means a ruled partition is used for the given design domain. The usual used grid mesh for the GS is rectangular and triangular, with polygon vertexes that are constant in the domain. Centroidal Voronoi tessellation (CVT) is a special condition of the Voronoi diagram, in which the generating point pi coincides with the centroid pic of the corresponding region (shown in Figure 5) [18]. This has wide applications in data compression, cell biology, the territorial behavior of animals, and the optimal allocation of resources. This special convex polygon has a random total edge range from 4 to 6, and their corresponding vertexes enlarge the search range of the truss orientation in the GS, which is advantageous for the searching strategy during the stiffener growth.  
Figure 5: A Voronoi tessellation (**a**) and a centroidal Voronoi tessellation (**b**).  
In this strategy, in order to obtain a 3D geometric mesh, a non uniform rational basis spline (NURBS) method is used by Rhino (3D model built software) to transform the parameters from a two-dimension plane (x, y) to the referenced three-dimensional surface (u, v). The basic mesh for the wing surface of the GRAND method is shown in Figure 6. After establishing the plane grid's relationship and setting the boundary and load conditions for the mesh, we use the optimization function defined in Equation (3) to obtain the bar connections and the bar size, respectively.
## 4 Analysis of the Initial Interface Cracking
### Width of the Opening  
The location of the opening is shown in Figure 14. The width of the opening was changed whilst maintaining the other parameters as constants to determine its influence on the horizontal displacement at the initial cracking of the interface. The horizontal center of the opening was always the same as the horizontal center of the infill wall. Figure 15 shows that when the infill wall was altered from no opening to openings of 1200 \(\times\) 1900, 2050 \(\times\) 1900, 2450 \(\times\) 1900, and 2850 \(\times\) 1900 mm, the horizontal displacement at the initial interface cracking varied from 1.25 mm to 1.43, 1.58, 1.88, and 2.53 mm, respectively. This finding implied that an increase in the width of the opening led to an increase in the horizontal displacement at the initial cracking of the interface and the delayed appearance of interface cracks.
## 4 Result and Discussion
### Agrometeorological Characteristics in Recent Years  
ET\({}_{0}\) is a useful indicator to examine the impacts of the meteorological drought on vegetation conditions. Using the daily ET\({}_{0}\) retrieved over the 500 m grid for the period between March and November of 2013-2019, a case study was analyzed to clarify the hydrological and meteorological characteristics of croplands in South Korea. We focused on the July and August of 2016-2018, during which heatwave and drought events occurred. From 12 July to 12 August, the land surface temperatures in 2016 and 2017 were typical (midday average, 32.4 \({}^{\circ}\)C and 32.2 \({}^{\circ}\)C, respectively). By contrast, the land surface temperatures in 2018 were remarkably high (midday average, 36.6 \({}^{\circ}\)C) (Figure 12). During that period, the average rainfall was 3.6 and 7.5 mm/day in 2016 and 2017, respectively, but only 0.8 mm/day in 2018. Because of the high temperatures and low rainfall, soil moisture decreased in 2018 (average, 29.9%) compared to 2016 and 2017 (average, 35.3% and 36.9%, respectively). The ET\({}_{0}\) values were higher in 2018 (average, 6.0 mm/day) than in 2016 and 2017 (average, 4.7 mm/day and 5.0 mm/day, respectively). The combination of high land surface temperature and ET\({}_{0}\) with low rainfall and soil moisture resulted in a reduced NDVI in 2018 (average, 0.72) than in 2016 and 2017 (average, 0.75 and 0.76, respectively). Figure 13 illustrates the NDVI from August 21 to 28 in 2017 and 2018, showing NDVI values with a different pattern. Insufficient rainfall leads to a deficit in the water supply to the land surface. When the higher ET\({}_{0}\) is combined with severe meteorologically dry conditions due to a heatwave, it can cause excessive water loss from the land surface that exceeds the water supply from rainfall and irrigation. Therefore, considering the well-developed irrigation systems used in croplands in South Korea, a small degradation in the greenness of croplands can be regarded as indicating a critical deficit of water supply and excessive water consumption in the croplands.  
Figure 11: Scatterplots of the ML-based ET\({}_{0}\) vs. KMA in situ data (**a**) and the MODIS PET vs. KMA in situ data (**b**) for the 8-day composite for croplands in South Korea for the 2013–2019 period.  
Figure 12: _Cont._  
Figure 12: Maps of the land surface temperature (LST), reference evapotranspiration (ET\({}_{0}\)), and normalized difference vegetation index (NDVI) for croplands in South Korea in July and August 2018.  
Figure 13: Maps of NDVI in the croplands in South Korea for August 21–28 of 2017 (**a**) and 2018 (**b**). The NDVI in 2018 was lower than in 2017 due to a heatwave, low rainfall, decreased soil moisture and increased ET\({}_{0}\). The red dashed line denotes the major agricultural areas in South Korea.
# A Prototype of a Portable Gas Analyzer for Exhaled Acetone Detection
## 3 Results and Discussion
### Correction for Temperature and Humidity  
Having calibrated the system, the actual measurements can be carried out. However, since the response of the sensor is not only a function of a detectable gas concentration but also temperature and humidity, it is necessary to correct the measured resistance \(R_{\mathrm{S}}\) for the last two parameters. This dependence exists regardless of the gas being measured and its concentration. Unfortunately, the required data is presented in datasheets only in a graphical form, which hinders analysis and is a source of systematic error. To allow for calculating the correction, a linear polynomial regression of two variables was used to model the temperature-humidity dependence out of the collected data points. A linear function describes accurately enough the relation while being very computationally inexpensive. The general formula is:  
\[corr(t,h)=c_{00}+c_{10}h+c_{01}t \tag{3}\]  
where \(t\) and \(h\) are the measured temperatures in \({}^{\circ}\)C and humidity in % at the time the sensor's resistance is measured while \(c_{xx}\) are constant coefficients estimated for a given sensor.
## 4 Results and Discussion
### Results: Pattern Extraction
#### 4.3.2 POS Patterns  
In the POS pattern technique, we combined the POS tags with clue words of the RS sentences. Because the combination of POS tags and the clue words increased the system accuracy. To evaluate the accuracy of the technique, all three guidelines (asthma, rhinosinusitis, and hypertension) were used in the experiment, and we achieved an accuracy of 71.86%, 73.67%, and 85.45%, respectively, as shown in Figure 6c.  
The results of Figure 6c depicts that the POS-based pattern did not perform well than the heuristic patterns. However, POS patterns are applicable on all CPGs irrespective of the CPG format. We achieved better accuracy than the POS without clue words, the primary reason was the generalization of the patterns along with clue words. However, some of the clue words may not be used in different guidelines. Therefore, a complete and generic solution is required to resolve the aforementioned problem. To remove this deficiency, we merged UMLS-based patterns into the proposed technique, which increased the system accuracy. The detailed results of the UMLS pattern are described in the following subsection.
# Electrochemical Biosensors Based on Conducting Polymers: A Review
## 3 Electroanalytical Methods
### Amperometry  
In amperometric conducting polymer-based biosensors, the current produced during the oxidation or reduction of an electroactive biological element at a constant potential that is applied between a reference electrode and a polymer-modified electrode is measured, thus providing specific quantitative analytical information. Such biosensors are inspired by the first and simplest amperometric biosensor developed by [PERSON] in 1956 who fabricated an amperometric oxygen sensor that produced a current proportional to the oxygen concentration when a potential of \(-0.6\) V vs. Ag/AgCl electrode was applied to a platinum electrode [83].  
The most widely studied amperometric biosensor is the glucose biosensor. In this system, glucose oxidase catalyzes the reaction of glucose with oxygen to produce gluconolactone and hydrogen peroxide. By monitoring the amount of hydrogen peroxide produced by this reaction in the presence of GOx through amperometric measurements, it is possible to determine the glucose concentration. In such biosensors, the GOx is immobilized in the conducting polymer either by electropolymerization of a solution containing a monomer and GOx or by addition of GOx in an electrodeposited conducting polymer film (see Section 4.1).  
Figure 2: Detection principles of conducting polymer-based electrochemical biosensors.
# Antijamming Improvement for Frequency Hopping Using Noise-Jammer Power Estimator
## 2 Proposed Estimator and Detector
### Noise-Jammer Power Estimator
\begin{array}{l}\sigma_{l,N}{{}^{4}}+2\mathrm{E} _{s}\mathrm{E}\Big{[}|h_{l}|^{2}\Big{]}\sigma_{l,N}{{}^{2}},\,w.o.\,jammer\\ \left(\sigma_{l,N}{{}^{2}}+\sigma_{l,J}{{}^{2}}\right)^{2}+2\mathrm{E}_{s} \mathrm{E}\Big{[}|h_{l}|^{2}\Big{]}\Big{(}\sigma_{l,N}{{}^{2}}+\sigma_{l,J}{{} ^{2}}\Big{)},\,w.\,jammer\end{array} \right.. \tag{5}\]  
Equations (3) and (5) both have noise, symbol, jammer powers, and channel gain. To estimate the pure noise-jammer power regardless of channel gain, \(\mathrm{E}_{s}\mathrm{E}\Big{[}|h_{l}|^{2}\Big{]}\) in Equation (3) needed to be removed. \(\mathrm{E}_{s}\mathrm{E}\Big{[}|h_{l}|^{2}\Big{]}\) was calculated by taking the square root of \(\Big{(}\mathrm{E}_{s}\mathrm{E}\Big{[}|h_{l}|^{2}\Big{]}\Big{)}^{2}\), which was obtained by subtracting the squared Equation (3) from Equation (5). Then, the proposed NJPE could be derived as  
\[ \begin{array}{ll}Z_{l}&=\mathrm{E}[X_{l}]-\sqrt{\mathrm{E}[X_{l}]^{2}- \mathrm{Var}[X_{l}]}\\ &=\left\{\begin{array}{l}\sigma_{l,N}{{}^{2}},\,w.o.\,jammer\\ \sigma_{l,N}{{}^{2}}+\sigma_{l,J}{{}^{2}},\,w.\,jammer\end{array}\right..\end{array} \right.\end{array} \tag{6}\]  
In Equation (6), if a jammer existed, NJPE estimated the sum of pure noise and jammer power; if not, NJPE only estimated pure noise power. Because the noise was generated from a low-noise amplifier (LNA) at the receiver, the noise powers of all slots were similar. Therefore, jammed slotswere easily detected by comparing with other slots. The estimation performance of the proposed NJPE is explained in Section 3.
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 4 ML for Outlier Detection
### Dbscan  
DBSCAN [34] follows a data clustering approach. Given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbours), marking as outliers points that are further away in low-density regions (whose nearest neighbours are too far away). Key strengths of DBSCAN, relevant to our work are: (i) it does not require setting up a specific number of clusters to detect; (ii) it can handle clusters of different shapes, sizes, and densities; (iii) it can identify global outliers; (iv) it does not need to have previous knowledge about the number of clusters to form, not even the format of its clusters.  
In terms of weaknesses, DBSCAN often misses the detection of varying density clusters and is known to not work well with high dimensional data. For the specific case of outlier detection, none of these disadvantages apply.  
DBSCAN requires two parameters as a base for its calculation. Firstly, it requires the size and boundary of its neighbourhood given by eps (\(\epsilon\)). Two points are considered to be neighbours if the distance between them is less than or equal to eps (\(\epsilon\)). Secondly, it requires the minimum number of points that the cluster can contain, given as MinPts and described in Equation (3):  
\[V\epsilon(x)=y\mid\delta(x,y)\ \leq\ \epsilon \tag{3}\]  
where \(V\epsilon(x)\) corresponds to the epsilon neighbourhood of a point x \(\in\) R given \(\delta(x,y)\) which corresponds to the Euclidean distance between two points x and y. Therefore, Equation (3) means mathematically that the neighbourhood of x with an \(\epsilon\) radius is formed by all the points y, given that the distance between these two points is less than \(\epsilon\).  
In Equation (4), the epsilon neighbourhood of point x must contain at least MinPts number of points.  
\[V\epsilon(x)\geq MinPts \tag{4}\]  
With eps and MinPts, DBSCAN classifies points according to their position in the vicinity of a point [34].  
DBSCAN categorises points as follows:  
* **Core point.** The point has at least A point is a core point if there are at least MinPts number of points (including the point itself) in its surrounding area with radius eps.
* **Border point.** The point is reachable from a core point and there are less than MinPts number of points (including the point itself) in its surrounding area with radius eps.
* **Outlier point.** The point is not reachable from any core point (and not a core point). The following concepts are the basis of the DBSCAN algorithm:
* **Direct density reachable:** A point \"A\" is directly density reachable from another point \"B\" if: 1. \"A\" is in the eps-neighbourhood of \"B\" and, 2. \"B\" is a core point.
* **Density reachable:** A point \"A\" is density reachable from \"B\" if there are a set of core points leading from \"B\" to \"A.
* **Density connected:** Two points \"A\" and \"B\" are density connected if there is a core point \"C\", such that both \"A\" and \"B\" are density reachable from \"C\".
# An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy
## 4 Case Studies
### ZDT Test Functions  
In order to prove the feasibility and validity of the improved NSGA-II algorithm proposed, the ZDT series of test functions [18] is selected to study the efficiency of the algorithm in solving multiple-objective optimization problems. These test functions are different, with different characteristics such as convex or concave, and their Pareto optimal frontier shapes are different, the specific descriptions and characteristics of the functions are shown in Table 1. Therefore, researchers often use these functions when conducting multi-objective optimization tests experiments. The simulation results are shown in Figure 2.  
In this section, to demonstrate the capabilities of the improved NSGA-II, we perform experiments on the test functions with the original NSGA-II and other methods, such as MOEA/D [19], IBEA [20] and SPEA2 [21], as comparison algorithms.  
The average values of the experimental algorithms for IGD and HV are presented in Tables 2 and 3, respectively. (Each result is the average of the two metrics calculated for 10 independent runs of the same algorithm over the same problem.)  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Function Name** & **Mathematical Formula** & **Function Characteristic** \\ \hline ZDT1 & \(f_{1}(x)=x_{1}\) & Convex, continuous \\  & where \(g(x)=1+9\cdot(\sum_{i=2}^{n}x_{i})/(n-1)\) & \\  & \(0\leq x_{i}\leq 1,i=1,2,\) Ln & \\  & \(f_{1}(x)=x_{1}\) & \\ ZDT2 & \(f_{2}(x)=g(x)\Big{[}1-(f_{1}(x)/g(x))^{2}\Big{]}\) & Concave, continuous \\  & where \(g(x)=1+9\cdot(\sum_{i=2}^{n}x_{i})/(n-1)\) & \\  & \(0\leq x_{i}\leq 1,i=1,2,\) Ln & \\  & \(f_{1}(x)=x_{1}\) & \\ ZDT3 & \(f_{2}(x)=g(x)[1-(f_{1}(x)/g(x))-(f_{1}(x)/g(x))\sin(10\pi x_{1})]\) & Concave, discontinuous \\  & where \(g(x)=1+9\cdot(\sum_{i=2}^{n}x_{i})/(n-1)\) & \\  & \(0\leq x_{i}\leq 1,i=1,2,\) Ln & \\  & \(f_{1}(x)=x_{1}\) & \\ ZDT4 & \(f_{2}(x)=g(x)\Big{[}\sqrt{1-f_{1}(x)/g(x)}\Big{]}\) & \\  & where \(g(x)=1+10\cdot(n-1)+\sum_{i=2}^{n}\big{[}x_{i}^{2}-10\cos(4\pi x_{i})\big{]}\) & Convex, multi-modal \\  & \(0\leq x_{1}\leq 1,-5\leq x_{i}\leq 5,i=2,\) Ln & \\ \hline \hline \end{tabular}
\end{table}
Table 1: ZDT test functions and characteristics.  
Figure 2: Solutions with INSGA-II on ZDT test function.  
It is shown in Tables 2 and 3 that the performance index of the INSGA-II algorithm is superior to four comparison algorithms on test functions. It is obvious from the above experiments that INSGA-II has better convergence accuracy and distribution performance.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.2.4 Dimensionality Reduction in Multiple Groups  
Once the maximum reduction in single group is determined, this section presents the result of the reduction in multiple groups. The results are based on the central group and combine it with other groups to analyze the influence on retrieval performance. The reason for choosing the central group is that in the process of sub-region division, there are more or less overlaps between central group and other groups which means that they have partial similarity in features and the feature loss in the multiple reduction process should be small. In the multiple reduction experiment, the \(K\) value is the optimal value in each group and the reduction operation is relatively independent, so there is no need to adjust the \(K\) value again. The experiment is conducted to prove all cases of multiple reduction starting from 1 reduction to the maximum. The result of the central and horizontal group is firstly shown in Table 5.  
The retrieval performance of multiple reduction is lower than the corresponding results of single group due to the increase of feature loss. When both groups reach the maximum reduction, the algorithm also has high retrieval efficiency which indicates that the reduction in central and horizontal group can be carried out simultaneously to achieve a total reduction of six dimensions with  
Figure 8: Comparison of horizontal reduction with other methods.  
a proportion of about 27%, more than 1/4 (Table 5). Moreover, the reduction in central and vertical group has similar results.  
The reduction in central and vertical group can also reach a total dimensionality reduction of four dimensions with a reduction rate of about 18% (Table 6). However, there is a significant decrease in retrieval performance, and it cannot be applied to large databases.  
In addition to the above two combinations, the fingerprint features in other cases are greatly different and the feature loss is quite large, which makes it difficult to guarantee the retrieval efficiency and fails to achieve ideal results. The horizontal group has more bits of fingerprint, that is to say, has more reduction space. Thus, the reduction in central and horizontal can get less data and better retrieval performance.
## 4 Simulation
### Single Tile  
Based on the proposed technique, a single-tile-based simulation experiment was performed to analyze the accuracy of the estimation of the counterpart location using a monopulse ratio curve with an improved linear section.  
Figure 15 shows the counterpart's location \((\theta,\phi)\), as set using a random function, along with the improved sum and difference patterns. The counterpart location based on the azimuth (\(\phi\)) is \(12.702^{\circ}\), whereas based on the elevation, (\(\theta\)) is \(91.711^{\circ}\). As a result of the simulation, the azimuth-based counterpart's location, as confirmed using the proposed technique, was confirmed to be \(12.689^{\circ}\). Accordingly, the azimuth-based location estimation error was found to be approximately \(0.013^{\circ}\).  
Figure 14: Beamforming results in (**a**) the presented tile (8 elements), with (**b**) 6 elements, (**c**) 4 elements, and (**d**) with the proposed method.  
Figure 15: Sum and difference patterns and counterpart locations in (**a**) azimuth and (**b**) elevation.  
To confirm the performance of the proposed technique, 100 simulation experiments were conducted for the estimation of the counterpart location based on a single tile. Figure 16 shows the counterpart's location determined based on random estimation, along with the azimuth-based estimation error estimated via the proposed method.  
The analysis results show that for 100 simulations, the average and maximum azimuth-based estimation errors are 0.019\({}^{\circ}\) and 0.046\({}^{\circ}\), respectively. The analysis results show that the counterpart's location estimation can be smoothly performed using the presented technique. For the analyzed data, the average azimuth-based estimation error is 0.046\({}^{\circ}\), which is larger than the MAE mean shown in Table 4, as determined based on the optimized monopulse ratio curve. This result reflects the influence of the counterpart's location when distributed within \(\pm 3^{\circ}\) based on the elevation.