esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Trends to Be Further Explored  
Emerging opportunities have the potential to further advance LULC and LULCC analysis (Figure 7). The dissemination of open science principles can favor the sharing of sample datasets, including in situ samples [44], a common practice that leads to progress in other scientific areas such as medicine. The publication of in situ sample datasets in open repositories as GitHub ([[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/))), Pangaea ([[https://www.pangaea.de](https://www.pangaea.de)]([https://www.pangaea.de](https://www.pangaea.de))), or Zenodo ([[https://zenodo.org/](https://zenodo.org/)]([https://zenodo.org/](https://zenodo.org/))), for example, has been growing, and the data can allow more accurate classifications [7; 16; 107]. This is part of the \"Land Cover 2.0 era\" described by [PERSON] et al. [47], where the transparency on the input data, algorithms, and the adoption, implementation, and communication of rigorous accuracy assessment protocols are reinforced.  
Figure 7: Scientific network mapping of the most used keywords in papers focusing on future possibilities in LULC and LULCC analysis. Colors indicate the cluster in which each keyword was more related. Lines represent co-occurrence link strength among terms.  
Currently, in situ data are not always available. In addition, in a long-term multi-temporal analysis approach, it is difficult to have samples for each year of interest [124; 125]. Various approaches have been used to overcome the gap of training samples, such as semi-supervised learning or active learning. However, they depend on preexisting labeled samples which require user expertise [125]. Especially for the agricultural sector, knowing crop distribution is useful for management and planning decisions. The choice of a sample during the data collection step impacts on a model's performance, but it is usually made via roadside surveys throughout the study area [132]. This becomes important when exploring ways to obtain representative training samples, and the open data policy with data sharing can address it. Consequently, it can boost the use of less frequent VIs, phenological metrics, hierarchical classifications, harmonized L8/OLI-S2/MSI data, environmental variables, and other strategies that deal with classification problems [71; 126; 129].
Sensors_**2013**, _13_, 1942-1964; doi:10.3390/s130201942  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Routing Protocol Based on Energy and Link Quality for Internet of Things Applications**  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{2,3}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\), [PERSON] \({}^{4}\) and [PERSON] \({}^{4}\)**  
\({}^{1}\) Federal University of Minas Gerais, Av. [PERSON], 6627, CEP 31270-901, Belo Horizonte, Brazil; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Federal University of Para, Av. [PERSON], 01, CEP 66075-110, Belem, Brazil; E-Mails: [EMAIL_ADDRESS] (D.R.); [EMAIL_ADDRESS] (E.C.)  
\({}^{3}\) Institute of Computer Science and Applied Mathematics, University of Bern, Neubruckstrasse 10, 3012, Bern, Switzerland  
\({}^{4}\) Federal University of Ceara, Campus do Pici, Bloco 942-A, CEP 60020-181, Fortaleza, Brazil; E-Mails: [EMAIL_ADDRESS] (A.N.); [EMAIL_ADDRESS] (J.N.)  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.:+55-31-3409-5589; Fax: +55-31-3409-5858.  
_Received: 15 December 2012; in revised form: 20 January 2013 / Accepted: 25 January 2013 / Published: 4 February 2013_  
**Abstract:** The Internet of Things (IoT) is attracting considerable attention from the universities, industries, citizens and governments for applications, such as healthcare, environmental monitoring and smart buildings. IoT enables network connectivity between smart devices at all times, everywhere, and about everything. In this context, Wireless Sensor Networks (WSNs) play an important role in increasing the ubiquity of networks with smart devices that are low-cost and easy to deploy. However, sensor nodes are restricted in terms of energy, processing and memory. Additionally, low-power radios are very sensitive to noise, interference and multipath distortions. In this context, this article proposes a routing protocol based on Routing by Energy and Link quality (REL) for IoT applications. To increase reliability and energy-efficiency, REL selects routes on the basis of a proposed end-to-end link quality estimator mechanism, residual energy and hop count. Furthermore, REL proposes an event-driven mechanism to provide load balancing and avoid the premature energy depletion of nodes/networks. Performance evaluations were carried out using simulation and testbed experiments to show the impact and benefits of REL in small and large-scale networks. The results show that REL increases the network lifetime and services availability, as well as the quality of service of IoT applications. It also provides an even distribution of scarce network resources and reduces the packet loss rate, compared with the performance of well-known protocols.  
keywords: energy-efficiency; internet of things; link quality; routing; wireless sensor network +
Footnote †: journal: Journal of Network Science
## 6 SAR for Antennas on Human Body
### Different Techniques to Reduce SAR Value
#### 6.1.2 SAR Reduction Using Metamaterials  
Metamaterials are frequently discussed for their unique physical properties and novel uses. Significant electromagnetic surface impedances in AMC structures can hinder surface current transfer and behave as a perfect magnetic conductor (PMC) at certain frequencies. In [100], to lower the SAR of the suggested PIFA antenna, the author employed an artificial magnetic conductor (AMC) with an implanted square metal patch, a dielectric substrate, and an underlayer. Figure 14 depicts an AMC designed with a 54 mm \(\times\) 24 mm \(\times\) 1.57 mm Taconic CRE-10 with a 3 \(\times\) 7-unit cell array. The SAR decreased by 43.3% after adopting the AMC framework [101].  
The design of a dual-band coplanar waveguide (CPW)-fed antenna was improved by using a metamaterial-based structure consisting of three layers: a separate metal layer acting as the ground plane beneath and a square metal ring surrounding a square metal  
Figure 14: Structure of metamaterials unit cell.  
Figure 13: Structure of electromagnetic band gap (P = periodicity; r = radius of unit cell; Er1 = periodicity of unit cell; Er2 = periodicity of substrate).  
patch on a dielectric substrate. At 2.45 and 5.8 GHz, respectively, this design produced a gain enhancement of 9.3 dB and 5.37 dB and an increase in the radiation efficiency of 48.4% and 35.7%. This structure led to a 70 percent reduction in the SAR [77].  
Advantages: Metamaterials can alter electromagnetic waves, which can potentially decrease the SAR by redirecting energy away from the body. They offer innovative solutions tailored to specific needs.  
Disadvantages: Metamaterials frequently entail higher costs and greater manufacturing complexities. Incorporating them into wearable devices could result in added mass, diminished flexibility, and elevated manufacturing expenses. Furthermore, it may be necessary to perform additional studies to investigate the long-term stability and performance of the subject under real-world conditions.
# A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
## 3 Results
### Results of the Antenna with AMC Backing
The same is illustrated in Figure 20a,b. The surface current distributions of the antenna with and without the AMC Backing are displayed in Figure 21. One can see from Figure 21a that the radiation was achieved more at the L-shaped slot at 0.915 GHz frequency. At 2.45 GHz, the radiation was achieved at the feed for the antenna without the AMC Backing. Figure 21b shows that the current radiated more throughout the antenna at 0.915 GHz. The radiation was believed to be along the T-shaped slot at 2.45 GHz frequency. The antenna radiated more when the antenna was placed above the AMC Backing at two frequency bands rather than when the antenna was without the AMC Backing.
## 2 Materials and Methods
### Methods  
Elemental analysis data were obtained from the Micro-Laboratory of ETH Zurich with the instrument Leco TruSpec Micro for C, H, N. The monomer conversion was determined from the solid content of the latex by using a HG 53 Moisture Analyzer from Mettler-Toledo. The particle size and the corresponding polydispersity index were measured by dynamic light scattering using a Zetasizer Nano ZS (Malvern Panalytical, Malvern, UK). The size of the obtained microclusters was determined by static light scattering (SLS) using a Mastersizer 2000. The click yield was calculated from FT-IR spectra comparing the ratios of the areas of the characteristic azide stretching band at 2100 cm\({}^{-1}\) and the aromatic C-H stretching band between 2800-3100 cm\({}^{-1}\). To do this, the sample (5 wt %) was mixed with KBr and analyzed using a Tensor 27 spectrometer (Bruker Optics, Billerica, MA, USA). Scanning electron microscopy was done using Leo 1530 SEM (Zeiss, Oberkochen, Germany) at a voltage of 5 kV equipped with a SE Inlens detector. The sample was coated with 4 nm platinum layer prior to the measurement. The pore size distribution was measured by mercury porosimetry using Pascal 140 and Pascal 440 (Thermo Scientific, Waltham, MA, USA). The BET measurement was done on a TriStar 3000 (Micromerities, Unterschleissheim, Germany).
## 2 Materials and Methods
### Growth and Biomass Production  
Cell density and dry biomass were monitored every third day using seven replicates for each sample (i.e., 8 \(\times\) 7 = 56/day). Cell counts were performed on a Neubauer chamber (VWR SCIENTIFIC, superior, VWR International, Radnor, PA, USA).  
Cell growth was evaluated using two models: the first one was via the logistic growth model, [22; 23; 24].  
\[dN_{t}/dt=rN[(k-N_{t})/k], \tag{1}\]  
where \(t\) is time (h); \(N_{t}\) is the cell density (cells mL\({}^{-1}\)) at time \(t\); \(r\) is the proportionality constant (h\({}^{-1}\)); and \(k\) is the carrying capacity of the system (cells mL\({}^{-1}\)) or maximum cell density (\(N_{max}\)) reached at the end of the exponential phase. The value \(k/2\), which is half the carrying capacity (cells mL\({}^{-1}\)), was also estimated, and \(t_{k/2}\) represents the estimated time (h) to reach half of the carrying capacity. For this model, the non-linear regression parameters were estimated via the iterative process to reach the least sum of squares (SSQ).  
Additionally, two other parameters were estimated. The first was maximum specific growth rate \(\mu\) (h\({}^{-1}\)), calculated by the equation [25; 26]:  
\[u_{max}=\ln(k/N_{1})/(t_{k}-t_{1}), \tag{2}\]  
where \(t_{k}\) is time at the end of the exponential phase, and \(t_{1}\) and \(N_{1}\) are time and cell density at the beginning of the exponential phase, respectively. The second parameter was population doubling time (h), estimated as, [27]:  
\[PDT=\ln 2/u, \tag{3}\]  
The second model was an exponential rise to the maximum of the form: \(N_{t}=N_{0}+a\left(1-\mathrm{e}^{(-b\cdot t)}\right)\), where cell density increase is represented by parameter \(a\), and estimated growth rate is estimated by parameter \(b\). For this model, time values were represented as days, thus at the beginning of the experiment:  
\[\left(\mathrm{t}=0\right)N_{t}=N_{0}, \tag{4}\]  
Nonlinear regressions were performed using the preset model for exponential rise to maximum, with three parameters, available in Sigmaplot Version 12.  
Dry biomass was determined every 48 h by filtering 10 mL of culture through pre-weighed and pre-burned GF/F glass-fiber filter, 0.7 \(\upmu\)m pore size (Whatman, Maidstone, UK). Biomass was further washed with 0.5 M ammonium formate to remove salt precipitates and dried at 60 \({}^{\circ}\)C for 24 h. The weight difference between the filter and the filter with the sample was determined.
## 4 Results
### Application to Real Biological Data
#### 4.2.1 Yeast Dataset  
The well-known synchronized yeast cell cycle data set of [33] includes 77 samples under various time during the cell cycle and a total of 6179 genes, of which 1660 genes are retained for this analysis after pre-processing and filtering. The goal of this analysis  
Figure 1: Boxplots of the scores \(R\) (see Section 4.1.2) obtained on simulated datasets using the standard and the greedy CORE-clustering algorithms as well as the Louvain and the Spectral clustering algorithms. A score of 1 reflects a purely accurate detection of the simulated clusters and the lower this score, the lower the accuracy. The boxplots were obtained by reproducing 30 times the procedure of Section 4.1.1. (**a**) Two simulated clusters with noise levels ranging from 0.25 to 1.5. (**b**) Same as (**a**) with five simulated clusters. (**c**) Five clusters simulated using 30, 15, 10 and 5 observations and a noise level of 0.5.  
is then to detect CORE-clusters among the correlation patterns in the time series of yeast gene expressions measured along the cell cycle. Using this dataset, a measure of similarity between all gene pairs was measured with the absolute value of Pearson's correlation. A total of about \(1.3\times 10^{6}\) weighted edges are then considered when representing the variables correlations in the graph \(G(N,E)\).
## 4 Experimental Results and Analysis
### NSL-KDD Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline SVM & 96.97 & 96.96 & 96.97 & 96.95 \\ KNN & 99.11 & 99.10 & 99.11 & 99.11 \\ [23] & 99.01 & 98.99 & 99.01 & 98.99 \\ [24] & 97.84 & 97.80 & 97.84 & 97.79 \\ [25] & 98.82 & 98.79 & 98.82 & 98.80 \\ [32] & **99.98** & - & - & - \\ CBC-IDS & 99.01 & 99.01 & 99.01 & 99.00 \\ CBA-IDS & 99.23 & 99.22 & 99.23 & 99.22 \\ CBF-IDS & 99.40 & **99.40** & **99.40** & **99.40** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of multi-classification for NSL-KDD. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Layer** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline CNN & 1 Conv & 97.51 & 97.63 & 97.51 & 97.54 \\
2 Conv & 98.54 & 98.51 & 98.54 & 98.49 \\  & 3 Conv & 99.10 & 99.08 & 99.10 & 99.09 \\ BiLSTM & 1 BiLSTM & 99.02 & 99.01 & 99.02 & 99.01 \\  & 1 Conv + 1 BiLSTM & 98.93 & 98.93 & 98.92 \\ CNN-BiLSTM & 2 Conv + 1 BiLSTM & 99.19 & 99.13 & 99.19 & 99.14 \\  & 3 Conv + 1 BiLSTM * & **99.40** & **99.40** & **99.40** & **99.40** \\ \hline \hline \end{tabular}  
* i.e., CBF-IDS.  
\end{table} 
Table 9: Multi-classification performance of different model structures for NSL-KDD. Figure 7: Performance of the CNN-BiLSTM model using different loss functions on the minority class U2R for NSL-KDD.
## 4 Performance Analysis and Evaluation
### Network Mobility Model  
Mobility of IP-sensor node and the SMAG domain network are the major advantages of IP-WSN over conventional static wireless sensor network. In this paper, mobility is the key concern in the design and performance analysis of the IP-WSN. The mobility model plays a key role in studying different mobility management strategies such as registration, handoff, and authentication, _etc_. A mobility model with minimum assumptions and simple to analyze is very useful for IP-WSN. Most of wireless network performance studies assume that the coverage areas are configured as a hexagonal or square shape. We assume that IP-WSN networks are to be configured with hexagonal topology. Sensor nodes for an IP-WSN area are assumed to have identical movement patterns within and across IP-WSN. A 2D square shaped random walk mobility model can be used to study the movement pattern of the movable sensor nodes. In this paper, we will use a network model subject to some modification for the five-layer personal area network model, with n = 5. In our network model, an IP-WSN consists of a cluster of 2D square shape of sensor nodes, as shown in Figure 13[15]. A brief description of Figure 13 is given in the following paragraph.  
Figure 13 shows a square shaped micro-cell and macro-cell configuration. Each macro-cell covers n \(\times\) n micro-cells (n = 5). Each macro-cell coverage area is one location area (LA). Firstly, we can aggregate the states of cells based on their symmetrical positions. Cells belonging to such aggregated state have the same properties. There are six aggregated states for the 5 \(\times\) 5 square shaped micro-cell/macro-cells. The corner states (S\({}_{11}\), S\({}_{15}\), S\({}_{51}\) and S\({}_{55}\)) are grouped into state S1. State S2 consists of (S\({}_{12}\), S\({}_{14}\), S\({}_{21}\), S\({}_{25}\), S\({}_{41}\), S\({}_{45}\), S\({}_{52}\) and S\({}_{54}\)), state S3 consists of (S\({}_{13}\), S\({}_{31}\), S\({}_{35}\) and S\({}_{53}\)), state S4 consists of (S\({}_{22}\), S\({}_{24}\), S\({}_{42}\) and S\({}_{44}\)), state S5 consists of (S\({}_{23}\), S\({}_{32}\), S\({}_{34}\) and S\({}_{43}\)), and state 6 consists of S\({}_{33}\). In this figure, aggregate states S1, S2 and S3 are the boundary states.  
Figure 13: Square shaped cell layout of five-sublayer PAN area model & state diagram.  
We define asterisk boundary states, S1*, S2* and S3*, which are in the LAs adjacent to the LA under consideration. Figure 13 also demonstrates the state transition diagram for the Markov chain. Movement into any boundary state indicates inter-IP-WSN mobility, which can be used to study binding update costs.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 4 The Proposed 5 GSBA Scheme
### Details of the 5 GSBA
\begin{tabular}{l l}
1. & **UE**\(\rightarrow\)**gNB:** UE sends an authentication request to the gNB with these steps: \\  & 1. & Generate a new random HMAC key \(K_{hmac}\), random ECDH public key \(a\cdot P\), and timestamp TS; \\  & 2. & Generate SUCI by encrypting \{SUPI, Y, \(K_{hmac}\}\) with \(PK_{core}\); \\  & 3. & Generate the next one-time hash secret \(Y_{2}\), and calculate its hash \(H(Y_{2})\); \\  & 4. & Update the \(Y\) in the local storage as \(Y_{2}\), as it will become the \(Y\) for the next authentication; \\  & 5. & Calculate \(\sigma 1=\) HMAC (\(\{ID_{gNB},\ SUPI,\ Y,\ H(Y_{2}),\ TS,\ a\cdot P\}\ K_{hmac}\)); \\  & 6. & Send the authentication request = \(\{SUCI,\ H(Y_{2}),\ TS,\ a\cdot P,\ \sigma 1\}\) to gNB. \\
2. & **gNB** checks the incoming authentication request with these steps: \\  & 1. & Check the validity of the timestamp TS, and then decrypt the SUCI into \{SUPI, Y, \(K_{hmac}\}\) using the private key \(SK_{core}\); \\  & 2. & Verify the HMAC of the message \(\sigma 1=\) HMAC (\(\{ID_{gNB},\ SUPI,\ Y,\ H(Y_{2}),\ TS,\ a\cdot P\}\ K_{hmac}\)); \\  & 3. & Fetch the latest transaction of the SUPI from the private blockchain locally; \\  & 4. & Compare the hash of the received \(Y\) with the \(H(Y)\) value stored in the blockchain. \\  & & If there is a collision (i.e., two values are equal), send an authentication response. \\  & 5. & Create a new blockchain transaction containing the value of \(H(Y_{2})\), and upload the block containing this transaction when the gNB is idle. \\
3. & **gNB**\(\rightarrow\)**UE:** gNB issues an authentication response to the UE with these steps: \\  & 1. & Generate a new random ECDH public key \(b\cdot P\); \\  & 2. & Calculate \(\sigma 2=\) HMAC (\(\{\)SUPI, TS, \(b\cdot P\}\), \(K_{hmac}\)), where TS is the received timestamp; \\  & 3. & Send the authentication response = \(\{\)TS, \(b\cdot P,\ \sigma\}\). \\
4. & **UE** checks the incoming authentication response with these steps: \\  & 1. & Calculate \(\sigma^{\prime}=\) HMAC (\(\{\)SUPI, TS, \(b\cdot P\}\), \(K_{hmac}\)). If \(\sigma\) equals to \(\sigma^{\prime}\), the UE continues to calculate the common ECDH session key using formula \(a\cdot b\cdot P\); \\  & 2. & Similarly, gNB calculates the common ECDH session key using formula \(b\cdot a\cdot P\). \\  & 3. & Since \(a\cdot b\cdot P=b\cdot a\cdot P\), a common session key is derived. Both parties are now mutually authenticated. \\ \end{tabular}
## 6 The Measurement Procedure  
The measurements were conducted with a precise step size of 0.02 nm. To achieve this, tunable laser light was skillfully coupled to the bus waveguide through a tapered lens fiber. The transmitted light was then efficiently collected via an output tapered lens fiber and subsequently directed to the power sensor housed within an Agilent 8163B Lightwave multimeter. The output power, a crucial metric in this analysis, was accurately gauged and visually represented on a computer screen. Facilitating this process was a sophisticated Python script leveraging the PyVISA library. The script seamlessly orchestrated the tuning of the laser source's wavelength and facilitated the real-time readout of the output power from the power meter. This synergy between the computer and the laser/power meter was made possible through the utilization of general-purpose interface bus (GPIB) equipment. The procedural sequence of the program was as follows: initiating the wavelength setting, incorporating a brief 1 s delay, measuring the output optical power, and culminating in the creation of a comprehensive CSV file encapsulating the acquired data. This well-orchestrated sequence of actions not only ensured precision in the data collection but also streamlined the subsequent analysis and interpretation of the RR transmission spectra. A schematic illustration of the measuring setup is shown in Figure 7.
## 3 Results and Discussion
### Effects of Different DMG Substitution Ratios on the Dual-Fuel Engine
#### 3.2.3 Emission Characteristics  
HC, CO, NOx, and soot are regulated emissions of a diesel engine. This section focuses on analyzing the effect of DMG addition on these emissions. Figure 6 illustrates the variations in emissions with different DMG substitution ratios.  
It is observed from Figure 6 that CO emissions decrease gradually when the DMG substitution ratios range from 0% to 15%. However, when the DMG substitution ratio exceeds 15%, there is a sharp drop in CO emissions. The influence of DMG on CO emissions can be summarized in two aspects. First, CO is one of the main components of DMG; therefore, the addition of DMG can increase the CO content within the cylinder. Secondly, the addition of DMG raises the in-cylinder temperature and pressure, which facilitates the oxidation of CO to CO\({}_{2}\). At lower substitution ratios, the impact of CO oxidation is partially offset by the introduction of DMG, resulting in a slow decline of CO emissions.  
Figure 5: Effects of different DMG substitution ratios on HRR: (**a**) Profiles of HRR against crank angle; (**b**) CA10-90 and CA50.  
However, at higher substitution ratios, the latter aspect becomes more prominent, leading to a significant reduction in CO emissions.  
The red line in Figure 6 demonstrates the variations in HC emissions with different DMG substitution ratios. It is evident that HC emissions increase initially at low substitution levels and subsequently decrease at relatively higher substitution levels. Similar results have been reported in the literature [28]. The reasons can be summarized as follows: At low substitution levels, the high-temperature area within the combustion chamber is relatively small. Consequently, the mixture in the low-temperature area becomes difficult to ignite, leading to an increase in unburned HC emissions. Conversely, at higher substitution levels, the wider range of the high-temperature area aids in reducing HC emissions. Additionally, the quenching distances of hydrogen and CO (carbon monoxide) are shorter compared to those of diesel. This means that the flame can propagate closer to the cylinder wall and into crevices effectively, thereby promoting a more complete combustion process and reducing HC emissions more effectively.