esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion
### Two Way Energy Utilization  
Figure 11 shows the two-way energy utilization of sensor nodes during the transmission of sensed data from CH to fog nodes and subsequently to the cloud server. This image shows how much energy is used from the CH to the fog node in S1 and the energy used from the fog nodes to the cloud server in S2. The results explain that communication of data from CH to the fog node consumed more energy because of no processing, filtering and refining of data here at the initial stage after being sensed. Additionally, when data reaches a fog node, it is refined and processed, appropriate computations are conducted, and operations are prompted here rather than transmitting the complete data set to the cloud server. Because of this, only the data that is not currently needed is transferred to the cloud server for processing and storage. As a result, sending data from a fog node to the cloud server, represented by S2 in the above diagram, uses less energy on average.  
Figure 10: Decoding of Data for Direct Transmission at Cloud End.  
Figure 9: Data Encoding from CH to Cloud Server.
# Assessment of Areal Methane Yields from Energy Crops in Ukraine, Best Practices
## 2 Materials and Methods
### Field Trials and Plant Material  
#### 2.2.1 Field Trials  
The analysed crops were cultivated in 2017 at the fields of the Agrarian Academy of Sciences of Ukraine. The location of the fields: Kiev and Kiev region (Vasylkiv district, urban-type settlement Grebinky; the research enterprise and research household \"Salyvinkivke\"; latitude 49.6\({}^{\circ}\) N, longitude 30.1\({}^{\circ}\) E, altitude 178 m). 18 varieties were cultivated in the Kiev region; 4 varieties were grown on the research fields of the Institute of Bioenergy Crops and Sugar Beet in Kiev.  
The fields belong to an area with a mean annual temperature of 10 \({}^{\circ}\)C. The crops were grown in a zone of unstable humidity with a mean annual precipitation of 341.1 mm.  
The crops were cultivated on the typical medium-loamy black soils with loessial loam and a humus range of 2.68% \(\pm\) 0.35%. The soils contained low-medium nitrogen (N) contents: 181.67 \(\pm\) 78.72 mg N per g air-dry soil. The pH of the soils was 6.64 \(\pm\) 0.09.  
During the crop cultivation period in 2017 at the field locations, there was a higher average monthly temperature and a lower average monthly precipitation in comparison to those values in the years 1985-2016 (see Figure 1). Due to the unfavourable weather conditions, the losses in DMY and thus in AMY could take place.  
As the amount of rainfall on-site was considered to be sufficient for cultivation, the field plots were not artificially irrigated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name of Plant** & **Climate** & **Carbon** & **Annual/Perennial** & **Class** & **Reference** \\ \hline Soybean & Cool season & C3 & Annual & Legume \\ \hline Maize & Warm season & C4 & Annual & Grass \\ \hline Sweet sorghum & Warm season & C4 & Annual & Thick-stemmed grass \\ \hline Sorghum cryzoidum & Warm season & C4 & Annual & Thick-stemmed grass [11; 16; 31; 32; 33; 34; 35] \\ or rice sorghum (soriz) & Warm season & C4 & Annual & Thick-stemmed grass \\ \hline Switchgrass & Warm season & C4 & Perennial & Thin-stemmed grass \\ \hline Miscanthus & Cool season & C4 & Perennial & Thick-stemmed grass \\ \hline Paulownia & Warm season & C3 & Perennial & Fast growth coppice \\ \hline \hline \end{tabular}
\end{table}
Table 1: The basic characteristics of the bioenergy crops.  
Figure 1: The comparison of weather conditions, such as monthly temperature and monthly precipitation, at the field locations during the cultivation period of the crop samples in 2017 with those values in years 1985–2016. Error bars indicate the monthly variability of temperature and precipitation in 2017.
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 3 GO Model Construction
### GO with Different Number of Rings  
GO can be seen as a derivative of graphene, that is, graphene is oxidized by a variety of oxygen-containing functional groups. Its adsorption on metal ions can be divided into two categories: physical adsorption and chemical adsorption. In terms of structure, the carbon atoms connected by an sp\({}^{2}\) hybrid are tightly packed into a single two-dimensional honeycomb graphene, which is mainly bound to metal ions through physical adsorption. In order to explore the influence of different numbers of six-membered rings in GO on structural stability and adsorption, graphene containing 4, 7, 10, 13, 16 and 22 six-membered rings (named GO4, GO7, GO10, GO13, GO16 and GO22, respectively) were constructed by GaussianView, their structure and energy in a stable configuration were calculated by Gaussian software, and their energy values are shown in Table 1.  
By observing the change in its energy, it is found that the GO energy decreases proportionally with the increase in the number of its six rings, indicating that the molecular structure gradually tends to be stable with the increase in the number of rings and the probability of occurrence is greater in practice. However, considering that the increase in the number of rings will greatly increase the calculation amount of the Gaussian method, the calculation amount will be greatly increased. Therefore, in the process of exploring GO, GO containing 10 six-membered rings was selected for simulation calculation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**The Type of GO** & **Energy/Hartree** \\ \hline GO4 & \(-\)618.0725 \\ GO7 & \(-\)961.2273 \\ GO10 & \(-\)1228.0654 \\ GO13 & \(-\)1534.1856 \\ GO16 & \(-\)1840.2942 \\ GO22 & \(-\)2376.2528 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The energy of GO containing different numbers of six-membered rings.  
Figure 1: Lerf–Klinowski model of GO.
## 5 Performance Analysis and Results
### Encryption Quality Metrics  
Encryption quality metrics for the proposed scheme are measured using deferential attack analysis, correlation analysis, histogram analysis, entropy analysis and key space analysis.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 8 Performance Analysis  
This section compares the proposed protocol and other relevant protocols regarding computational and communication costs. This section also compares the security features of the proposed protocol with those of existing protocols.
## 1 Introduction
Overall, those experimental systems are playing an important role as precursors for operational spaceborne imaging spectroscopy missions, but the amount and quality of data are unfortunately insufficient for a wide range of potential application fields of imaging spectroscopy. The Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission [38, 39, 40, 41] is intended to cover this gap in spaceborne imaging spectroscopy for Earth observation. EnMAP is a joint response of German Earth observation research institutions, value-added resellers and space industry to the increasing demand for accurate, quantitative information about the status and evolution of terrestrial ecosystems. After a competitive and successfully accomplished definition phase, EnMAP was approved by the German Aerospace Agency (DLR in the German name) in the beginning of 2006 and is currently in the construction phase with launch planned for mid-2018. EnMAP is expected to become a key system in the field of spaceborne imaging spectroscopy in potential co-existence with other comparable and complementary missions, such as the Japanese Hyperspectral Imager Suite (HISUI) [42], the Italian PRISMA (Hyperspectral Precursor of the Application Mission) [43], the USA's Hyperspectral Infrared Imager (HyspIRI) [44], the French HYPXIM [45] and the Italian-Israeli SHALOM (Spaceborne Hyperspectral Applicative Land and Ocean Mission). Even though no coordination between different missions for the optimization of acquisitions is foreseen, imaging spectroscopy users will benefit from the larger diversity of data sources. In addition, the Sentinel-2 [46] and Landsat-8 [47] multispectral systems will also co-exist with EnMAP and share a similar spectral coverage, spatial sampling and focus on land applications. Those two multispectral missions hold the prospect of becoming ideal partners for EnMAP in the development of synergetic applications exploiting the wide spatial coverage and short revisit time of Sentinel-2 and Landsat-8 together with EnMAP's continuous spectral sampling of the VNIR and SWIR regions. This contribution presents a review of the EnMAP mission and ongoing preparatory activities. An overview of the mission requirements and internal organization is provided in Section 2. The main modules of the EnMAP space and ground segments are described in Sections 3 and 4. Ongoing activities towards the development of critical technical and scientific mission components are discussed in Sections 5 and 6. Finally, a short summary of the main aspects covered throughout the text is provided in Section 7.
## 1 Introduction
In this case, it would be desirable to have a control over these applications with the minimum configuration and using a common interface. App adaptivity is intended to solve these challenges, providing a framework for the development of IoT applications that are:  
* portable
* personalised
* adapted to changing contexts
* created intuitively  
This work is based upon well-established standards so as to avoid a new vertical silo in the IoT. This approach is intended to offer a novel framework for developing portable, personalised and adapted to changing contexts CoAP-based applications with a minimal configuration. The IoT comprises other application protocols such as MQTT, DDS and AMQP [4]. App adaptivity is a component-based system, thereby these protocols can also be part of the underlying IoT infrastructure with new integration components. It should be noted that this work uses an efficient access of sensors and actuators through CoAP functionalities such as the observe operation and the group management, which could not be found in other IoT protocols. However, the integration of IoT protocols would enable the interaction with non-CoAP systems, e.g., an actuation with an external system. The developed applications offer a set of behaviours than can be updated or created based on the device changes during the system execution, adapting applications to the environment changes. The application development is done with a visual tool where users can intuitively create flows, following action sequences. It has been shown how visual environments provide a better user experience, alongside a higher perceived success and a reduced perceived workload [8], leading to better results [9]. Last but not least, the security has been analysed over the life cycle of the system and a secure solution using standard protocols that covers all the components involved has been proposed. The rest of the paper is organised as follows. An introduction of CoAP is given in Section 2. In Section 4 we discuss related work on device-decoupled applications. The problem statements and research goals of this work are highlighted in Section 3. Section 5 presents an overall overview of the App adaptivity architecture and its components. Then, the implementation of App adaptivity is provided in Section 6. The App adaptivity evaluation has been done in Section 7. Section 8 discusses the differences between CoAP, the CoAP++ framework and App adaptivity. Lastly, Section 9 presents our conclusions and outlines future work.
# Multidisciplinary Approach to Characterizing the Fingerprint of Italian EVOO
## Materials and Methods
### Small Sensor System (S3)  
The state-of-the-art of S3 was completely designed, developed, and validated at the SENSOR Lab (University of Brescia, Brescia, Italy) in collaboration with Nano Sensor Systems (NASYS) s.r.l., a spin-off of the University of Brescia, Italy. It has been successfully applied in some selected applications [21; 22; 23; 24; 25; 26; 27]. The S3 system consists of a pneumatic part, an electronic part, an incubated sensor chamber housing an array of eight MOX gas sensors, and an online data acquisition and processing app. The flow sensors, temperature and humidity sensors, and actuators (valves and pumps) are all embedded inside the system. The multi-sensor array is connected with an autosampler HT2010H for the headspace sampling, supporting a 42-loading-site carousel. Table 3 shows MOX sensors details of S3 device; only sensors produced at SENSOR Lab are listed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials (Type)** & **Composition** & **Morphology** & **Operating Temperature** (\({}^{\circ}\)C) \\ \hline \multicolumn{4}{c}{**Phase 1**} \\ \hline SnO\({}_{2}\)Au (n) & SnO\({}_{2}\) functionalized with Au clusters & RGTO & 400 \({}^{\circ}\)C \\ SnO\({}_{2}\) (n) & SnO\({}_{2}\) & RGTO & 300 \({}^{\circ}\)C \\ SnO\({}_{2}\) (n) & SnO\({}_{2}\) & RGTO & 400 \({}^{\circ}\)C \\ SnO\({}_{2}\)Au + Au (n) & SnO\({}_{2}\) grown with Au and & & \\ functionalized with gold clusters & & & \\ SnO\({}_{2}\)Au (n) & SnO\({}_{2}\) grown with Au & & \\ CuO (p) & CuO & & \\ \hline \multicolumn{4}{c}{**Phase 2**} \\ \hline SnO\({}_{2}\)Au + Au (n) & SnO\({}_{2}\) grown with Au and & & \\ functionalized with gold clusters & & & \\ SnO\({}_{2}\)Au + Au (n) & SnO\({}_{2}\) grown with Au and & & \\ functionalized with gold clusters & & & \\ CuO (p) & CuO & & \\ SnO\({}_{2}\) (n) & SnO\({}_{2}\) & RGTO & 450 \({}^{\circ}\)C \\ SnO\({}_{2}\)Au (n) & SnO\({}_{2}\) with gold clusters & RGTO & 400 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 3: Type, composition, morphology and operating temperature for S3 sensors made at the SENSOR Laboratory. Sensors are divided for the two different phases of analysis.  
In each phase, commercial sensors were used, too. In phase 1, a TGS2611 was placed side-by-side with nanowires and RGTO sensors; in phase 2, five commercial sensors were used: MQ3, MQ8, MQ9, TGS2620 and TGS2611. In Figure 5, images of nanowires SnO\({}_{2}\)Au sensor are shown: on the left, there is a photo of the sensor with its gold support; on the right, a Scanning Electron Microscope Zeiss -LEO 1525 (Oberkochen, Germany) image where the nanowires can be seen.
# Monitoring Electrochemical Reactions in Situ with Low Field NMR: A Mini-Review
## 1 Introduction  
The uses of low-field time domain NMR in research and development have been steadily increasing over the years [1; 2; 3; 4; 5; 6]. Nowadays, it is used for several purposes, such as the quality analysis of industrial products as well as the study of physical and chemical properties of a wide array of samples, both solid and liquid, such as vegetable oil, fuels, reservoir rocks, foods and biological tissues [1; 2; 3; 4; 5; 6]. The instruments used for these analyses are normally based on permanent magnets which produce a magnetic field intensity usually below 0.6 T (25 MHz for \({}^{1}\)H) with a low homogeneity (above 100 ppm) which means that the relevant information can only be extracted from the intensity of the free induction decay signal (FID), the spin echo or by measuring the relaxation times \(T_{1}\) and/or \(T_{2}\) or the diffusion coefficient of the sample. In this review we show the application of time domain NMR (TD NMR--also known as low-field NMR) to an increasingly popular research field which is the in situ monitoring of electrochemical reactions (EC-NMR) [7; 8; 9]. The first published paper which detailed the use of TD NMR for such an application was published as recently as 2010 [10] (which is a 40 year delay from the first paper detailing the use of high-field NMR for the same application [11]). This work showed that TD NMR is suitable for monitoring the electrodeposition reaction of copper complexes using the transversal relaxation time, \(T_{2}\). Since then, TD NMR has been shown to be suitable for monitoring the electrodeposition of copper ions using both H-type magnets as well as unilateral magnets. Furthermore, it has been observed that reactions performed in situ show a higher reaction rate than those performed ex situ due to a phenomenon known as magnetolectrolysis [12; 13] which occurs as a result of the interaction between the magnetic field and the ion flow present during the experiments. This effect increases the mass transport within the solution during the experiment. In this review, we show the methods used to measure the concentration of paramagnetic ions in solution using TD NMR, the challenges of in situ EC-NMR and the solutions to those challenges (whenever theyexist) as well as the influence of the magnetic field from the spectrometer on the magnetoelectrolysis. The use of inductively coupled coils to improve the signal-to-noise ratio of the NMR signal during in situ electrochemical experiments is also shown here.
## 2 The Proposed SK Diagnosis Technology
### Diagnosis Effectiveness Estimation  
Characterization of technology diagnostic effectiveness is achieved by means of the Fisher criterion (FC) [28], following authors in refs. [4; 15; 29; 30; 31; 32; 33; 34; 35; 36], who also applied the FC to measure a technology diagnostic effectiveness. Thus, we are able to directly compare diagnostic technologies and to evaluate a diagnostic effectiveness, using the FC. The FC is a statistical measure that uses the ratio of the between-class scatter to the within-class scatter to indicate the level of feature separation between damaged and undamaged conditions. For validation of the SK based diagnostic features, the FC is applied as [28]:  
\[FC(\theta)=\frac{(\mu_{D}(\theta)-\mu_{ND}(\theta))^{2}}{\sigma_{D}^{2}(\theta) +\sigma_{ND}^{2}(\theta)} \tag{8}\]  
where \(\mu\) and \(\sigma^{2}\) are, respectively, the mean and variance of the diagnostic features estimated for all segments, D and ND indicate damaged and undamaged conditions, respectively.  
The novel decision-making method to rolling element bearings, based on the k-nearest neighbors (kNN) and the majority rule [24; 37], is applied in this study. The method considers as training and testing diagnostic features the maximum values of squared envelope from each segment. The approach first creates M one-dimensional clusters of the fault free training data by using the k-means method. After clustering, the calculation of the novelty scores for testing data is done in the following five steps:  
* The nearest neighbor distance dNN1\({}_{\text{ij}}\) is calculated for each training data sample xij in the cluster \(j\), where \(i=1\),...,\(N_{j}\) and \(j=1\),...,\(M\). \(N_{j}\) is the number of samples in cluster j and M is the number of clusters.
* The k maximal NN distances among the \(N_{j-1}\) NN distances are used for each cluster and their mean value \(D_{ij}=E_{max}dNN_{1 ij}\) is calculated.
* Euclid distances between testing data sample \(y_{i}\) and training data samples \(x_{j}\) in cluster \(j\) are calculated.
* The averaged kNN distance dNN2 for cluster \(j\) and test data sample is then calculated as the mean value of k minimal NN distances.
* The novelty score of the test data sample for cluster \(j\), \(NS_{ij}\), is calculated as the ratio between distances \(D_{1}\) and \(D_{1}\) and average kNN distance \(dNN_{2}\) as \(NS_{ij}=dNN_{2 ij}D_{1j}\) and the minimal novelty score for sample \(y_{i}\) is selected.  
After decisions are obtained, the total probability of correct diagnosis is used to estimate diagnosis effectiveness.
## 2 Islanding Detection Principles and Methods
### Islanding Operation in Distribution Grids
The accuracy of IDM is high when the NDZ is low and vice versa. Active (\(\Delta\)P) or reactive power (\(\Delta\)Q) mismatches between the generation and the consumption in the islanded part of the network can be used to evaluate NDZ. The oscillations in frequency or voltage following the islanding event will not change enough to detect the islanding operation if the power mismatches P and Q at PCC are too low. Figure 3: An example of a microgrid.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 2 Standard Gray Wolf Optimization Algorithm and Fitness Function
### Fitness Function  
Path planning is a crucial step in achieving autonomous navigation for mobile robots, entailing the discovery of a collision-free route from a start point to a destination while meeting specific requirements. In the path optimization process, two core issues are considered: path length, which reflects the algorithm's optimization capability, and obstacle avoidance, which ensures safety. Therefore, this paper designs the fitness function of the proposed algorithm to incorporate these two aspects: path length and obstacle avoidance.  
Path length is a key metric for evaluating the quality of a route. Suppose a route is represented by a series of points, denoted as \(P=\left\{P_{1},P_{2},P_{3}\cdots,P_{n}\right\}\), where each point's coordinates are given by \(\left(x_{i},y_{i}\right)\). The path length can be obtained by summing the Euclidean distances between consecutive points, as in Formula (9):  
\[F_{1}=\sum_{i=1}^{n-1}\sqrt{\left(x_{i+1}-x_{i}\right)^{2}+\left(y_{i+1}-y_{i} \right)^{2}} \tag{9}\]  
In order to ensure the safety of the path, obstacle avoidance must be considered in the fitness function. The safety of path planning is enforced by introducing a penalty value, which depends on the number of obstacles traversed by the path, as in Formula (10):  
\[F_{2}=\kappa O_{b} \tag{10}\]  
where \(\kappa\) is the penalty factor, which is used to regulate the weight of obstacle avoidance, and \(O_{b}\) is the number of obstacles that the path crosses.  
Combining the path length with the avoidance penalty value, the integrated fitness function can be expressed as follows:  
\[F=\sum_{i=1}^{n-1}\sqrt{\left(x_{i+1}-x_{i}\right)^{2}+\left(y_{i+1}-y_{i} \right)^{2}}+\kappa O_{b} \tag{11}\]  
This design of the fitness function has wide applicability in the field of path planning. By comprehensively considering the path length and obstacle conflicts, the robot is able to plan efficient and safe moving routes in complex environments and improve its navigation capability.  
Such a fitness function design is widely applicable in path planning. By simultaneously considering path length and obstacle conflicts, the robot can plan efficient and safe trajectories in complex environments, thereby enhancing its navigation capabilities.
## 3 Results and Discussion
### Validation of Pressure Measurement: Experimental vs. Simulation-Based CP Prediction (Youn's Model)
(**d**) Simulated stress map in CLO3D, indicating pressure distributions at pattern size reductions of 5%, 10%, 15%, and 20%. The results of the simulated pressure obtained from the CLO3D built-in feature and [PERSON]'s CP model, compared to the experimental values, are shown in Figures 7 and 8, respectively. [PERSON]'s CP model demonstrated closer alignment with the experimental values, with differences ranging from a minimum of 0 kPa (SJP2 at the applied strain of 5%) to a maximum of 1.9 kPa (INTP2 at the applied strain of 20%). In Figure 7, the CLO3D built-in result shows an unrealistically overestimated value for INTP 2 and SJP2, especially above the applied strain of 15%. In contrast, in Figure 8, [PERSON]'s CP model provides more accurate pressure predictions than the built-in CLO3D feature, likely due to incorporating additional mechanical properties such as the fabric thickness, stretch stiffness, and specific material properties, which are particularly observed in SJP2 and INTP2. The small differences in the pressure values suggest that the CP model could be a valuable tool for future developments in compression garments if they incorporate [PERSON]'s CP model. Figure 7: Comparison of simulated pressure values from the CLO3D built-in pressure feature and experimental pressure measurements obtained from PPS and Kikuhire, with and without electrode application, using four different samples: (**a**) SJ2, (**b**) SJP2, (**c**) INT2, and (**d**) INTP2.