esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
## 2 Materials and Methods
### S3 Analysis Conditions
For SnO\({}_{2}\) sensors, the powders are mounted in the middle of the furnace at 1370 \({}^{\circ}\)C, and the inert air flow at temperatures between 350 and 400 \({}^{\circ}\)C is used as a carrier from the furnace to the substrate where nanowires begin to develop [30]. S3 was previously used, with considerable success, in numerous studies applied to the field of food technology and quality control [31; 32]. The output of the S3 analysis consists of the sensors' resistance variation due to the interaction of VOCs with the sensing elements. The exposure to VOCs lasted 1 min, while 9 min passed to restore sensors' baseline. Prior to analysis, the sensors' responses in terms of resistance (\(\Omega\)) were standardized relative to the first value of the acquisition (R0). This standardization was performed for each measure so that for all the sensors, the first point value was 1, smoothing differences in the starting value of resistances between the measures themselves. For all the sensors, the difference between the first value and the minimum value was determined during the time of analysis; thus, the value \(\Delta\)R/R0 was derived. These features were used as input for principal component analysis (PCA). Here, PCA has been used to visualize the data cluster. By contrast, a hard approach to quantify the accuracy of the system was employed. In particular, the k-nearest neighbor (k-NN) algorithm was used with 5-fold crossvalidation technique. The aim of crossvalidation is to test the model's ability to predict new data that were not used in estimating it, in order to counteract overfitting or selection bias. The accuracy provided is the mean of the accuracies of the 5 steps of prediction.
# Positioning Information Privacy in Intelligent Transportation Systems:
## 2 Solutions for Spotting of Vehicles on the Road
### Global Navigation Satellite Systems  
One of the GNSSs is the Global Positioning System (GPS) which is a technology, network, and service owned and maintained by the USA [38]. The GPS service provides end-users with an opportunity to estimate their position and to retrieve globally synchronized time. Entire GPS architecture consists of space, control, and user segments [39]. The space segment is comprised of a constellation of 31 satellites (as of March, 2019), not including the decommissioned, on-orbit spares. GPS satellites operate in medium Earth orbit (MEO) at an altitude of approximately 20,200 km (11,550 miles) [40]. The GPS control segment is comprised of a global network of ground stations designed for tracking the constellation of satellites in MEO and also monitoring their transmissions, communicating and analyzing the data. The user segment consists of end users (civilian and military).  
Figure 1: General example of vehicular positioning techniques.  
The second GNSS giant is the Russian Global Navigation Satellite System (GLONASS) which serves the same role despite some minor differences [41]. GLONASS satellite orbits are arrayed in three planes, separated from one another in right ascension of ascending node by 120 degrees, with eight satellites in each plane. The number of spacecraft is 26 including 24 actively operational ones (as of March, 2019), and the altitude is 19,100 km (11,868 miles) [42].  
Finally, GNSS positioning technology widely used in the European Union is GALILEO starting 2005 [43]. GALILEO system currently has 26 operational satellites on the orbit placed in 3 orbital planes, at 29,600 km (18,400 miles) altitude [44]. Worth to note, the GNSS system specifically utilized in China-BeiDou Navigation Satellite System (BDS). BDS constellation currently has 17 operational satellites, and the number is expected to reach 35 by 2020 [45]. Nonetheless, most of the mass-produced vehicles are already equipped with both GPS and GLONASS systems [46].  
The fundamental principle of using the GNSS system is to determine the location by measuring the timing of the reception of a synchronized signal from the navigation satellites [47; 48]. Despite almost-the-entire-globe coverage, GNSS has a number of challenges that could not be neglected.  
To start with, the material penetration characteristics limit the use of GNSS in hard-reach places such as underground parking, tunnels, etc. Next, because of the near-spherical shape of the Earth calculating an accurate distance between two points requires the use of spherical geometry and trigonometric math functions. However, many applications calculate an approximate distance using simplified ones. Thus, the GNSS-based calculation of the distance between two objects can lead to errors of 10 percent or more [49]. The most significant issue of the GNSS is its propagation in urban environments where multipath fading has a tremendously adverse effect on the position estimation.
# Low-Cost Active Anomaly Detection with Switching Latency
## 3 The DMSC Policy
### Performance Analysis
#### 3.3.1 Performance Analysis for Scenario 1  
Here, we assume that the parameter \(s\) satisfies \(s=o(B)\) as \(\alpha\to 0\). In this subsection, we prove that the DMSC policy is asymptotically optimal in this scenario.  
**Definition 1**.: _We say that policy \(\Gamma\) is asymptotically optimal in \(\alpha\) if_  
\[\lim_{\alpha\to 0}\frac{E(C|\Gamma)}{C^{*}}=1. \tag{24}\]  
**Theorem 1** (Asymptotical Optimality of DMSC Policy).: _Let \(E(C|DMSC)\) denote the expected cumulative cost incurred by the DMSC policy. If \(s=o(B)\), the expected cumulative cost satisfies_  
\[\lim_{\alpha\to 0}\frac{E(C|DMSC)}{C^{*}}=1. \tag{25}\]  
_In other words, the expected cumulative cost of the DMSC policy is asymptotically optimal in \(\alpha\)._  
Proof.: The proof idea is briefly provided here.  
In Appendix A.1, we show that the objective function on the expected cumulative cost has an asymptotic lower bound and gives an expression for the asymptotic lower bound. Then, in Appendix A.2, we upper bound the expected cumulative cost of the DMSC policy by establishing the upper bounds of the number of observations and switchings of each stage. In addition, we show that if \(s=o(B)\), the expected cumulative cost under DMSC policy approaches the asymptotic lower bound as \(\alpha\to 0\), which completes the proof. The detailed proof is given in Appendix A.3.
## 4 Numerical Simulation
### Model Construction  
Using the Longlang Expressway in Loudi city, Hunan Province, as the base project, ABAQUS software was used to establish a three-dimensional slope model with a length of 25 m, width of 7 m, height of 13 m, and slope of 1:1.5. Microbial reinforcement treatment was carried out along the vertical slope direction, and the reinforcement thickness was 1 m. The model assumed that the soil was an ideal elastic-plastic body, and the Mohr-Coulomb model was used. According to the experimental results, the soil before microbial reinforcement is taken as density \(\rho\) = 1.78 g/cm\({}^{-3}\), elastic modulus E = 10 MPa, Poisson's ratio \(\mu\) = 0.35, cohesion c = 13 kPa, and internal friction angle \(\varphi\) = 11.7\({}^{\circ}\), and after microbial reinforcement is taken as density \(\rho\) = 1.96 g/cm\({}^{-3}\), elastic modulus E = 10 MPa, Poisson's ratio \(\mu\) = 0.35, cohesion c = 25.95 kPa, and internal friction angle \(\varphi\) = 15.6\({}^{\circ}\). The cohesion and internal friction angle vary with the field variables, and the range of field variables varies between 0.5 and 3. The boundary conditions are set in ABAQUS load to constrain the displacements in the x, y, and z directions at the bottom of the model, constrain the displacements in the y directions before and after the model, constrain the displacements in the x directions to the left and right of the model, and apply the gravity force in the z-axis direction to the model as a whole. The mesh division of the slope model adopts eight-node linear hexahedral cells (C3D8), and the calculation area is divided into a total of 2415 cells.
# A Novel Method for Modeling the Electromagnetic Characteristics of Switched Reluctance Motors
###### Abstract  
Accurate measurement and determination of the electromagnetic characteristics of the switched reluctance motor (SRM) are indispensable to their advanced control and performance analysis. This paper proposes a novel method for modeling the flux-linkage characteristics of SRM. This strategy without rotor locking, includes three steps, namely measuring, estimating and calculating steps. In the first step, flux linkage curves with current at four equilibrium positions are obtained by an indirect measurement method. In the second step, the flux-linkage characteristic of SRM in the middle position is estimated by its two adjacent flux-linkage curves. In the third step, a decoupled analytical model based on the five-order Fourier series is developed to construct the entire flux-linkage characteristics. The proposed method can not only eliminate the requirements on rotor clamping devices or position sensors, but also achieve fast and accurate modeling for the electromagnetic characteristics of SRM. Comparative studies are carried out between the proposed method and the conventional rotor clamping method, and the results demonstrate that the proposed method has a good agreement with the conventional rotor clamping method. Moreover, the simulation model is established in MATLAB/Simulink software based on the entire electromagnetic characteristics calculated by the proposed method. The accuracy of the proposed method is further verified by comparing the simulation and experimental results under different operating modes of the SRM.  
switched reluctance motor; electromagnetic characteristic; equilibrium position; Fourier series Article
# Surface Gloss, Radiopacity and Shear Bond Strength of Contemporary Universal Composite Resins
## 2 Materials & Methods
### Radiopacity Measurements  
Five cylindrical specimens (8 mm diameter and 1 mm height) were prepared from each CR, paying attention to its homogeneity and non-porosity. One freshly extracted human molar was used to compare enamel and dentin radiopacity. The use of extracted human teeth was approved by the non-interventional clinical research ethics board of Hacettepe University (Approval Number 2020/20-86). The molar tooth was sectioned longitudinally using a slow speed diamond saw (Isomet1000, Buehler, Lake Bluff, IL, USA) to obtain a 1-mm thick slice and the tooth slice was stored in distilled water.  
A 99% pure aluminum stepwedge with 9 incremental steps measuring 1 mm was used. Five specimens of each CR, together with the aluminum stepwedge and tooth slice, were positioned over the CMOS sensor ([PERSON], Soredex, Milwaukee, WI, USA), and the storage phosphor plate system (VistaScan, Du'rr Dental, Bitigheim-Bissingen, Germany) on the radiographs. All specimens were placed in a dental X-ray unit (for 32 s, 65 kVp/7 mA, Myray, Cefla Dental Group, Imola, Italy) at a distance of 30 cm.  
The mean gray values of the CRs and enamel and dentin substrates (tooth slice) were measured in digital radiographs using Adobe Photoshop CS3 Extended computer program, version 10.0 (Adobe Systems, San Jose, CA, USA). To reduce the measurement deviation, five different regions without air bubbles or other anomalies, each with an area of 10 \(\times\) 10 pixels, were measured. The measurements were carried out by an evaluator who was blinded to the brands of CRs.
# Lessons for Coastal Applications of IMTA as a Way towards Sustainable Development: A Review
## 3 Sustainability Potentials of IMTA
### Economic Sustainability  
Economic sustainability requires the use, recycling, and protection of human and material resources in ways which create sustainable values over the longer term [45]. Economic sustainability also calls for a production system that meets consumption needs without jeopardizing future requirements [46]. Human life on Earth is nourished and perpetuated by utilizing the limited natural resources available [47]. The profit component of sustainability is concerned with achieving economic growth, resource efficiency, and the financial viability of businesses [48].  
Blue mussel (_Mytilus edulis_) near commercial salmon farms in the Bay of Fundy, Canada, developed 20 percent faster than those at reference locations further away, indicating that harvested mussel production is higher in an integrated aquaculture system than in a monocultural one [49]. Evidence from the BIOFAQs project on the west coast of Scotland supports this. Here, mussel growth was considerably higher at a site within 10 m of a salmon farm than at a control site 500 m away [50]. The mussel lines acted as in situ biofilters, and increased growth of the mussels was linked to the use of organic waste from the salmon farm. Economic research has focused on the diversity of benefits for growers, consumers, and society that result from the implementation of IMTA systems [51]. A study of South African abalone farming shows that IMTA can stabilize seafood supply by increasing product diversity and reducing market risks from price volatility, as well as increase job diversity by providing high-pay jobs for trained personnel while offering lower-pay jobs for untrained people in peripheral locations [37]. Invertebrates, seaweeds, and detritivorous fish may be economically beneficial in terms of supplying local consumption needs, while high-value fish and shrimp are exported for foreign currency. Implementing IMTA improves waste assimilation capabilities at farm level and in the wider environment. This may be a main reason why IMTA today is practised most widely in China [52; 53].
# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 3 Materials and Methods
### Prediction of PQP Using LSTM
#### 3.3.1 Process Capability Control Analysis  
PPD can be categorized as lower and upper specification limits for a machine as well as the amount, condition values, and start generation and end times for a product made from the machine. PPD is converted into data shown in Table 3 using the composite statistical analysis methods mentioned from Section 3.2.  
Before applying LSTM to predict PQP, the outcomes of composite statistical analysis in Table 3 will be preprocessed, as follows:  
* Encoding categorical data: the label encoder and one hot encoder are used to convert categorical data to numerical data. The categorical data are variables that contain label values rather than numeric values. Many machine learning and deep learning algorithms require all input and output variables to be numeric; therefore, the categorical data must be converted to numerical values.
* Splitting dataset: dataset is divided into three subsets: training, validation, and testing sets. The training set is the actual data used to train the model, that is, to fit the parameters. The validation set is the dataset used to tune the hyper-parameter and evaluate a given model fit on the training dataset. The testing set is the dataset used when a model is completely trained, to evaluate how a final model fits the training set.
* Feature scaling: This step is also known as data normalization and is used to normalize the range of independent variables or features of data. Feature standardization is used to scale values by applying Z-score normalization. This method is widely used for normalization in machine learning and deep learning models. It calculates the distribution mean and standard deviation for each feature. Then, it subtracts the mean from each feature. After that, it divides each value of each feature by its standard deviation.
## 3 Results and Discussion
### Optimization of PEF Pretreatment and Microwave-Convective Drying  
The most optimal process parameters, i.e., specific energy intake (pulsed electric field pretreatment) and microwave power (microwave-convective drying), were selected using RSM, based on specific criteria. Several responses were assigned a'minimize' goal (drying time, water activity, \(H_{72h}\), \(EC_{50}\) ABTS, and \(EC_{50}\) DPPH). In the case of RR, SSL, and TPC, the main goal was to maximize these responses. Both factors and all responses had the same importance. The program identified the 13 most optimal combinations of process parameters under the above-mentioned criteria, and the best solution was selected based on  
Figure 6: Response surfaces of ability to scavenge: (**a**) ABTS**+ radical cations (\(EC_{50}\) ABTS); (**b**) DPPH**+ radicals (\(EC_{50}\) DPPH) by apples treated with PEF prior to microwave-convective drying, depending on the microwave power (factor A) and specific energy intake (factor B).  
the highest value of desirability. Specific energy intake equals 3.437 kJ/kg, and microwave power of 300 W turned out to be the most optimal parameters (desirability = 0.624). Such a combination of parameters would provide the following response values, predicted from the model: drying time: 62 \(\pm\) 6 min; water activity: 0.188 \(\pm\) 0.010; \(H_{72h}\): 1.2419 \(\pm\) 0.0055; RR: 2.2538 \(\pm\) 0.1158; SSL: 0.7275 \(\pm\) 0.0269; TPC: 1257.08 \(\pm\) 140.44 mg GAE/100 g d.m.; \(EC_{50}\) ABTS: 0.3045 \(\pm\) 0.0699 mg d.m./mL; \(EC_{50}\) DPPH: 0.9072 \(\pm\) 0.1666 mg d.m./mL. Research by other authors [57] aimed at optimizing the microwave-convective drying of apples proved that the most desirable quality attributes of the obtained dried materials were achieved at the highest analysed microwave power.
## 4 Experimental Results
### Application Protocol Performance  
In the previous section, we showed that the sensors are able to execute the FRBSs that have been adapted to them. In this section, the aim is to measure the main characteristics of the distribution of the KB among the sensors of a WSN by means of the application protocol described in Section 3.3.  
This application protocol has been implemented in the 2.33 release of the network simulator ns2 [32] and configures the network and nodes with the same characteristics as the Sun SPOT network and nodes to measure the delay and throughput of the KB distribution.  
The WSN was configured with 25 sensors uniformly scattered in a 100 m by 100 m area, with the PAN coordinator located in the center. The size of the KB was 340 Bytes. The nodes were configured using the transport protocol RadioStream (simulated using TCP), the routing protocol for the _ad hoc_ network AODV, a link and MAC layer IEEE 802.15.4 (2.4 GHz), a transmit power of 0 dBm, a receiver threshold of -77 dBm, and a receiver sensitivity -90 dBm. During the simulation, the PAN coordinator sent the KB to the sensors sequentially.  
In these conditions, the time to distribute the KB to all sensors was 33 s, and the throughput remained around 15 KB/s (120 Kbps) during the simulation. This distribution time is sufficient for the real-world application presented in this work; sensors can only transmit the results of their own inferences to neighboring sensors after the initial distribution.
## 5 Discussion and Conclusions  
Here, we have derived the effective bending stiffness of multilayered plate by considering surface stresses as in the Steigmann-Ogden model of surface elasticity. Unlike the Gurtin-Murdoch model, this model explicitly takes into account the surface bending stiffness. As a result, the derived formula contains various contributions related to the elastic response in the bulk, the influence of the surface Lame moduli and of the surface bending stiffness moduli. For the \(N\)th-layered plate, the effective bending stiffness depends on \(N\) (see (26) or (27)), so the influence of the surface moduli is more pronounced, especially for plates of nano-sized thickness. Here, we restricted ourselves to the determination of the effective bending stiffness and the analysis of the surface bending stiffness influence. The complete theory of multilayered plates can be developed using layer-wise theory as in [34; 35], where \(D_{\text{eff}}\) can be considered as a global stiffness characteristic of the whole laminate plate.  
The bending of a multilayered plate presents a natural example of the influence of the surface bending stiffness, which is a peculiarity of the Steigmann-Ogden model. Among other examples of this kind, let us mention the bending of dominant beam-lattice structures such as those considered in [18] for the Gurtin-Murdoch model, or porous materials such as those analyzed in [2; 36; 37; 3]. Due to their very good prospective properties, such beam-lattice structures have found various applications in engineering; see, e.g., [38; 39; 40; 41] for pantographic beam-lattice materials including pantographic beams [42; 43; 44; 45] and plates [46; 47; 48]. Indeed, by considering surface elasticity, we can see that this may change the response at the micro- and nanoscale; thus, for beam-lattices at small scales, as presented in [49], the surface bending stiffness may play a dominant role.  
Conceptualization, V.A.E.; methodology, V.A.E.; software, V.A.E.; validation, V.A.E. and T.W.; formal analysis, V.A.E.; investigation, V.A.E. and T.W.; writing--original draft preparation, V.A.E. and T.W.; writing--review and editing, V.A.E.; visualization, T.W.; supervision, V.A.E.; project administration, V.A.E.; funding acquisition, V.A.E. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant from the Government of the Russian Federation (contract No. 14.Y26.31.0031). The APC was funded by the International Research Center for Mathematics & Mechanics of Complex Systems (M&MoCS) of the University dell'Aquila, Italy.  
The authors declare no conflict of interest.  
Figure 3: Dimensionless bending stiffness \(\overline{D}\) vs. number of layers \(N\).