esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Spatial Zoning in the Su-Xi-Chang Region
#### 4.1.3 Model Accuracy of Spatial Zoning  
The USAI is used in our study to divide the city into functional zones. We adopted the density of construction land within a certain neighborhood so as to characterize the intensity of human development activities. Theoretically, the higher the intensity of human development activities, the more prominent the urban spatial characteristics of urban agglomerations. In addition, a positive correlation between the intensity and the characteristics is obvious in numerical statistics.  
Furthermore, we adopted the construction land ratio (%) of each grid unit through spatial superposition analysis of the 500 m \(\times\) 500 m grid unit and land use data of the Su-Xi-Chang region from 2018. Based on this, we calculated the ratio of construction land within a 5 \(\times\) 5 neighborhood of each grid unit. In order to verify the quality of the USAI, 17,446 sample points evenly distributed in the Su-Xi-Chang region were selected with 500 m taken as an equal distance. Using IBM SPSS Statistics 24, we conducted a cubic polynomial regression analysis on the USAI and the intensity of human development activities. Regression results obtained through the calculation of the sample point data are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Area (km2)** & **Percentage (\%)** \\ \hline Core Zone & 2113 & 9.61 \\ Fringe Zone & 2432 & 11.06 \\ Suburb Zone & 17,443 & 79.33 \\ Total & 21,988 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Area and proportion of each partitioned space.  
Figure 4: Model results: (**a**) The USAI distribution map; (**b**) Spatial zoning of the Su–Xi–Chang region.  
The intensity of human development activities is presented as \(X\), while the urban spatial characteristic of the USAI is shown as \(Y\). Polynomial regression analysis was performed for both and the obtained fitting model is shown in the following formula.  
\[Y=1.786+0.779X-0.023X^{2}+0.00086X^{3} \tag{12}\]  
correlation coefficient \(r^{2}\) is 0.516, sig = 0.000 < 0.01. Rejecting the nihilistic hypothesis, the regression analysis fitting model has significant nonlinear characteristics. This model shows that the urban spatial characteristics of the Su-Xi-Chang region significantly correlate with the intensity of human development activities, so the fitting effect of the model is applicable. Therefore, the USAI model is suitable for representing the urban spatial characteristics of the urban agglomerations.
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 1 Introduction
Section 2 introduces the distinct material properties of graphene in different frequency bands with a tunable Fermi level, which lays the physical foundation for graphene-based active metasurfaces and offers different mechanisms of the tunability. Section 3 summarizes the dynamic graphene phase shifters with the target to achieve a large range of dynamic phase response for desired wavefront shaping working in transmission, reflection, in-plane, and out-of-plane manners. Section 4 is focused on the polarization manipulation to achieve tunable polarization selectivity and polarization conversion. Conclusions and personal remarks on the challenges and development directions of graphene metasurfaces are given in Section 5.
## 4 Materials and Methods
### Measurement of Processing Time  
To evaluate the processing time associated with each system, a detailed work protocol was developed and provided to three laboratory operators, each with over 10 years of experience in filtration analysis. Each operator was tasked with processing five different samples using both systems, carefully recording the time taken for each stage of the process. The recorded times were then subjected to a comparative analysis to identify any significant differences in the temporal performance of the two evaluated devices.
# Context-Based Energy Disaggregation in Smart Homes
## 4 Disaggregation Algorithm
### Inference  
As mentioned above, [PERSON] and [PERSON] [9] in 2012 proposed an approach based on additive Factorial Hidden Markov Model that aimed at improving inference complexity performances and avoiding local optima issues [9]. As the number of devices to disaggregate grows; in fact, the evaluation of all the possible HMM evolutions that could have generated the aggregate output, implies an increase in the computational complexity of the disaggregation process. Therefore, the authors proposed an algorithm called Additive Factorial Approximate MAP (AFAMAP) which is able to bypass the unreachable exact inference through the approximation of the Maximum _A Posteriori_ Probability [9]. [PERSON] and [PERSON] [9] have released a Matlab version of the AFAMAP algorithm (2012); in their paper, they provide some test results and discuss the effectiveness of the algorithm compared to other inference algorithms (_i.e._, Maximum _A Posteriori_ Probability, Structured Mean Field, _etc._) in terms of disaggregation error.  
For simplicity, we do not quote the mathematical model as it is available in detail in [9] with some comparative results.
## 5 Results and Analysis
### Analysis and Discussion
#### 5.4.2 Fed-in Power to the Grid  
Four diagrams in Figure 12 illustrates the power fed into the grid. It can be seen from Figure 12a that the spring day hardly provided any wind energy to the grid while the wind power was totally used to supply the domestic demand or charge the energy storage system. The winter day fed the most wind energy to the grid, as shown in Figure 12d. This is because that the wind power generated was more than the local need on that day. There are two periods on the summer day that the wind power was fed into the grid, as given in Figure 12b. One period is from 4:00 to 6:00 prior to the morning peak demand. Another one started at the peak wind power at 20:00. Most of the fed-in wind power occurred between 12:30 and 16:30 where \(ESOC_{S}\) was fully charged. As can be seen in Figure 12c, the extra power from the wind turbine was fed into the grid between 12:00 to 17:00 while \(ESOC_{S}\) kept the upper limit with no need for charging or discharging the storage system.
## 2 Piezo-Actuated FSM System
### Hysteresis of Piezo-Actuated FSM System  
For angular control of the piezo-actuated FSM, the mirror angle needs to be measured in the two rotational degrees of freedom. Strain gauge sensors were used to obtain a direct measure of the actual mirror angle based on the strain of the piezo-stack actuators. The mechanical stroke range and hysteresis of the FSM motion system were measured by applying a 1-Hz sinusoidal signal with 15 V amplitude to the piezo-actuated FSM amplifier input of one system axis at a time. The measurement results of both axes (tip and tilt direction) are depicted in Figure 4, demonstrating a mechanical stroke range of approximately \(\pm 5\) mrad in both tip and tilt direction. The maximum hysteresis is about 15% for both system axes.  
Figure 4: Measured hysteresis for piezo\(-\)actuated FSM: (**a**) tip direction; (**b**) tilt direction.  
Figure 3: Modal analysis of the piezo-actuated FSM with the optical mirror, showing the first main resonance (tip) mode and the second main resonance (tilt) mode.
## 4 Mathematical Modeling  
### System Dynamics  
In this section, our aim is to propose a linear time-invariant (LTI) system with the general form:  
\[\dot{x}=Ax+Bu \tag{5}\]  
\[y=Cx+Du \tag{6}\]  
For the set of first-order differential equations, which were mentioned in Section 2, the general structure of a four-echelon serial SC is integrated by factory (\(F\)), distributors (\(D\)), wholesalers (\(W\)), and retailers (\(R\)), as presented in Figure 3.  
The mixing problem conditions of the SC dynamics present two cases: (1) when the inflow is equal to the outflow (\(\varphi_{i}=\varphi_{o}\)), and (2) when inflow is greater than outflow (\(\varphi_{i}>\varphi_{o}\)).  
_Case 1: \(\varphi_{i}=\varphi_{o}\)_  
In this case, there exists a balance between the production rate at each node, which implies \(\varphi_{i}=\varphi_{o}\). At each stage of the serial supply chain, for case 1, the assumption is that the inflow is equal to the outflow for production rates. In general, a single-input, single-output (SISO) mathematical model for each node takes the following form:  
\[\frac{dI_{0}}{dt}=\varphi_{i}-\varphi_{o}\frac{I_{o}}{C_{o}} \tag{7}\]  
Expanding Equation (7), for each echelon of the four nodes of the SC, we have:  
\[\frac{dI_{1}}{dt}=\varphi_{i}-\varphi_{1}\frac{I_{1}}{C_{1}} \tag{8}\]  
\[\frac{dI_{2}}{dt}=\varphi_{1}\frac{I_{1}}{C_{1}}-\varphi_{2}\frac{I_{2}}{C_{2}} \tag{9}\]  
\[\frac{dI_{3}}{dt}=\varphi_{2}\frac{I_{2}}{C_{2}}-\varphi_{3}\frac{I_{3}}{C_{3}} \tag{10}\]  
\[\frac{dI_{4}}{dt}=\varphi_{3}\frac{I_{3}}{C_{3}}-\varphi_{4}\frac{I_{4}}{C_{4}} \tag{11}\]  
_Case 2: \(\varphi_{i}>\varphi_{o}\)._  
In this case, the production rates at each node follow the condition \(\varphi_{i}>\varphi_{o}\), and the capacity control presents the following relation, where \(q(t)\) corresponds to the concentration of the outflow and \(r_{out}\) to the output rate from the mixing problem:  
\[R_{out}=q(t)r_{out} \tag{12}\]  
Considering that:  
\[q(t)=\frac{I}{C_{o}+(\varphi_{i}-\varphi_{o})t} \tag{13}\]  
Analyzing each node with the condition of \(\varphi_{i}>\varphi_{o}\), we have:  
\[\frac{dI_{1}}{dt}=\varphi_{i}-\varphi_{1}\frac{I_{1}}{C_{1}+(\varphi_{i}- \varphi_{1})t} \tag{14}\]  
\[\frac{dI_{2}}{dt}=\varphi_{1}\frac{I_{1}}{C_{1}}-\frac{I_{2}}{C_{2}+(\varphi_{ 1}-\varphi_{2})t} \tag{15}\]  
\[\frac{dI_{3}}{dt}=\varphi_{2}\frac{I_{2}}{C_{2}}-\frac{I_{3}}{C_{3}+(\varphi_ {2}-\varphi_{3})t} \tag{16}\]  
\[\frac{dI_{4}}{dt}=\varphi_{3}\frac{I_{3}}{C_{3}}-\frac{I_{4}}{C_{4}+(\varphi_ {3}-\varphi_{4})t} \tag{17}\]  
Figure 3: Generic four-echelon serial SC realization for a capacity–inventory management model.  
In Equations (14)-(17) the assumption is that the inflow is higher than outflow for production rates at each stage of the serial supply chain. This is the case for case 2, in which we have an accumulation of inventory levels.
# COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
## 2 Materials and Methods
### Methods and Modeling Strategy
#### 2.2.2 AnFIS  
ANFIS falls as a member of the artificial neural networks (ANNs). The architecture of ANFIS is improved through the integration of the Takagi-Sugeno fuzzy system, i.e., a variation of fuzzy logic [86; 87]. The ANFIS was coined in the early 90s and gained a widespread popularity within the research community for scientific modeling and estimation. This method was developed in the early 1990s. As ANFIS hybridizes the ANNs and Takagi-Sugeno fuzzy system, it benefits from the high performance of the both computing and learning technique for dealing with nonlinear functions [86; 87]. Figure 3 presents the architecture of the developed ANFIS model.  
As illustrated in Figure 3, ANFIS has five main layers [88; 89; 90]. The first layer is the inputs layer, which takes the parameters and imports them to the model. This layer is also called the input layer of the fuzzy system. The outputs of the first layer import to the second layer and carry prior values of membership functions (MFs). Fuzzy rules are concluded from the nodes on the second layer devoted to the degree of activity. Furthermore, the ANFIS's third layer multiplies the degree of activity of any rules. The fourth layer normalizes the functions and nodes and further produces the outputs [91] and sends them to the output layer. The important factors for determining the accuracy of ANFIS are the number and type of MFs, the optimum method, and the output MF type [92; 93; 94; 95; 96].  
Model of ANFIS is implemented through MATLAB's ANFIS toolbox. To inititilaze the model, the input parameters are set as the independent variables of each scenario, and the output variable was the number of cases or mortality rate. The ANFIS model was trained with three triangular, trapezoidal, and gaussian MFs. This step was performed in order to select the best MF. The output membership function type selected linear type because of its ability to further reduce errors. Training of FIS was done with optimum backpropagation (BP) method and 0 value of error tolerance.  
Figure 3: Architecture of the developed ANFIS.
###### Abstract  
Active packaging with CO\({}_{2}\)-emitters (pads) has recently been used for shelf-life extension of fresh fish. The aim of this study was to identify consumer attitudes towards fresh fish packaging, to examine whether Greek consumers prefer active packaging with pad over active packaging without pad, to investigate any perceived differences in the sensory freshness of the fish, and to relate consumer perception to volatile composition of fish fillets. In total, 274 consumers participated in the study which included freshness sensory evaluation of gillehead seabream (_Sparus aurata_) and seabass (_Dicentrarchus labrax_), whole-gutted and filleted, raw and cooked, at high quality and at the end of high-quality shelf-life. Samples were packed under modified atmosphere either with a pad or without. Results showed that consumers preferred packages with pads, especially at the end of high quality shelf-life. They perceived raw samples packed with a pad to be fresher and closer to the ideal product, and also had a higher purchase intention towards them. Cooked samples were not perceived differently. Consumers' perception was in accordance with the GC-MS findings in the volatile compounds that function as freshness or spoilage indicators. Most participants were positive towards fresh fish packaging although they usually buy unpacked fresh fish. Our results suggest that active packaging with CO\({}_{2}\) emitters contribute to freshness preservation and that it has a positive potential in the Greek market.  
consumer acceptance; CO\({}_{2}\)-emitter; sensory freshness; fish shelf life; modified atmosphere; volatile compounds 2023
# Wake-Up Receiver with Equal-Gain Antenna Diversity +
## 3 Hardware
By inserting a third antenna switch, it is possible to use both antennas as input and output. Due to the three extra antenna switches, the node has an additional power consumption of less than \(3\,\mathrm{\SIUnitSymbolMicro A}\) compared to the power consumption of the node introduced above. Of course, the additional components introduce some extra costs. However, the benefit of combining the low-frequency signals over combining the high-frequency signals using a classical equal-gain diversity technique that uses, for example, a Wilkinson splitter, clearly is the not required phase-control circuit as visualized in Figure1. As shown above, destructive interference of the low-frequency signals is virtually not possible. Figure 7 illustrates the block diagram of the sensor node. As discussed above, the low frequency wake-up receiver AS3932 has a typical sensitivity of \(100\,\mathrm{\SIUnitSymbolMicro W}\) RMS (root mean square). Figure 5: Simplified illustration of the signal chain starting with the on-off-keying modulated high-frequency carrier signals received by the two antennas, the low-frequency wake-up signals after demodulation and the combined wake-up signal.
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
###### Abstract  
This article provides a general overview of the communication protocols used in the IEC61850 standard for the automation of electrical substations. Specifically, it examines the GOOSE and R-GOOSE protocols, which are used for exchanging various types of information. The article then presents real cases of cyber attacks on the industrial sector, highlighting the importance of addressing cybersecurity in the IEC61850 standard. The text presents security drawbacks of the communication protocols mentioned earlier and briefly explains two algorithms defined in the IEC61850 standard to address them. However, the authors suggest that having only a couple of algorithms may not be sufficient to ensure digital security in substations. This article presents a study on the cryptographic algorithms ChaCha20 and Poly1305. The purpose of the study is to experimentally verify their adaptation to the strict time requirements that GOOSE must meet for their operation. These algorithms can operate independently or in combination, creating an Authenticated Encryption with Associated Data (AEAD) algorithm. Both algorithms were thoroughly reviewed and tested using GOOSE and R-GOOSE frames generated by the S-GoSV software. The computational time required was also observed. The frames were analysed using the Wireshark software. It was concluded that the algorithms are suitable for the communication requirements of electrical substations and can be used as an alternative to the cryptographic algorithms proposed under the IEC61850 standard.  
IEC61850; IEC62351; Generic Object-Oriented Substation Events (GOOSE); R-GOOSE; ChaCha20; Poly1305; AEAD
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 4 Proposed Routing Protocol
### Cluster Formation
Again, if the \(CNRF\)'s highest value is more than 1, then the initial node asks the CN to join as CM where the ID of the neighboring node is minimum. Else, the \(UN_{n}\) states as CN directly with its ID and \(UN_{i}\) asks \(UN_{n}\) to join the cluster as CM. If the Channel Number (N) is less than 1. the \(UN_{i}\) requests to join \(UN_{n}\) as CM. If the initial ID (\(ID_{i}\)) is less than the neighboring ID (\(ID_{n}\)) then \(UN_{i}\) states as CN and form a cluster. Else it requests \(UN_{n}\) to join as CM where the \(ID_{n}\) is minimum. In our cluster formation approach, each group is led by a central node (CN). This CN acts as a hub for all communication within the group, so other nodes, called Cluster Members (CMs), rely on it to send and receive messages. It organises clusters in a way that maximizes communication efficiency. Once a cluster is formed, the CN decides on the best frequencies for communication within the group. If neighboring clusters are detected, the CN assigns some CMs as Nearest Nodes for forwarding nodes (NNs) to improve connectivity with those clusters. NNs help relay messages between different clusters, enhancing overall network performance. Clusters typically consist of a CN, a Secondary Central Node (SCN), and CMs. However, since the network environment can change rapidly, the proposed protocol is to handle nodes joining or leaving clusters. These protocols ensure that our network remains stable and efficient, even as it adapts to dynamic conditions.
# DEM Simulation of the Load Transfer Mechanism of a GRPS Embankment with a Fixed Geogrid Technique
## 2 Establishment and Calibration of DEM Model
### Modeling of the Subsoil between Piles  
Consistent with the selected model tests by [PERSON] et al. (2019), the microparameters of the subsoil between the piles were calibrated using a compression test for a specimen with a width of 550 mm and a height of 150 mm. In this study, the subsoil was simulated by disk particles with a diameter of 3-5 mm that were uniformly distributed. Similarly, the unbonded linear contact model was used for the interparticle contact of the subsoil between the piles. The compression specimen was generated by the layered compression method ([PERSON] et al., 2019), and the porosity of the initial specimen was controlled at 0.14 until the model reached a stable state. Same as the laboratory test, the compression loading rate \(v_{\text{c}}\) of the top wall was \(10^{-6}\) m/s.  
The comparison of vertical stress versus settlement between the DEM simulation and the physical test is shown in Figure 3. In the laboratory test, the growth rate of the settlement of the subsoil between the piles increased with increasing surcharge load, which indicated the yielding of the soil during compression. It is noteworthy that the unbonded particles in this study were used to simulate the subsoil between the piles, while the foam was used in the physical model. Although the non-linear change in the strength of the foam material was not reflected in the numerical model, the load in the numerical model was similar to the laboratory compression test within the settlement range of 0-30 mm. In the laboratory model tests, the settlement of the soil between the piles was always less than 30 mm, as reported by [PERSON] et al. (2019). It should be noted that the settlement of the subsoil between the piles is important for the development of the soil arching effect. The subsoil between the piles in the physical model was simulated using foam, and the purpose of the unbonded assembly in the numerical simulation was to reproduce the compression-settlement characteristics. Within the tolerance range (0-30 mm), the compression-settlement characteristics of the numerical model and the physical model were essentially consistent.  
Figure 2: Comparison of the numerical and experimental results for the biaxial tests.
###### Abstract
The application of energy methods enables the establishment of closed-form equations for the LTB of simply supported prismatic I-section beams and parametric equations for prismatic cantilevers with I-sections. However, since the buckling mode shapes of the web-tapered I-beams and tapered cantilevers with I sections are more complex, forming a closed-form simplified equation may be impossible for transverse load types varying moment distribution along the beam, considering their position on the cross-section. No study has provided simplified equations to determine the LTB load of web-tapered mono-symmetric I-beams under various transverse loads and load positions. The LTB assessment of web-tapered I-beams is typically conducted using finite element analysis (FEA) with solid and shell elements. However, FEA involves numerous steps requiring specialized expertise and substantial computational resources. Therefore, using FEA for the rapid evaluation of LTB for web-tapered I-beams may be impractical. This study first developed an analytical treatment based on the Ritz method to compute the LTB load of simply supported web-tapered beams with doubly symmetric and mono-symmetric I-sections. The analytical model accounts for web tapering, I-section monosymmetry, transverse load types, load height level, and slenderness effects. The model has been implemented in Mathematica v11.0 software [46]. Therefore, the LTB load of web-tapered I-section beams can be instantly calculated using the Mathematica package by simply defining the geometric dimensions and material properties (elastic modulus and Poisson's ratio). The LTB loads calculated by the developed Mathematica package have been validated against shell-based FEA results obtained using ABAQUS/CAE v2021 software [47].