esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Main Results
### Guidance  
This section is devoted to designing the ILOS guidance to calculate the approaching angle with respect to the desired path. Then, the time derivative of Equation (7) is taken to obtain the position errors dynamics  
\[\begin{cases}\dot{x}_{e}=u_{r}\cos(\psi-\gamma_{p})-v_{r}\sin(\psi-\gamma_{p})+ y_{e}\dot{\gamma}_{p}+\theta_{x}-U_{pp}\\ \dot{y}_{e}=u_{r}\sin(\psi-\gamma_{p})+v_{r}\cos(\psi-\gamma_{p})-x_{e}\dot{ \gamma}_{p}+\theta_{y}\end{cases} \tag{10}\]  
where \(\theta_{x}=V_{c}\cos(\beta_{c}-\gamma_{p})\) and \(\theta_{y}=V_{c}\sin(\beta_{c}-\gamma_{p})\); \(V_{c}=\sqrt{V_{x}^{2}+V_{y}^{2}}\leqslant V_{c\max}\) and \(\beta_{c}=\text{atan2}(V_{y},V_{x})\). By using the kinematic relationship given by Equation (8), we have \(\dot{\theta}=U_{pp}/\sqrt{\dot{x}_{d}^{2}+\dot{y}_{d}^{2}}\). In addition, the virtual target velocity \(U_{pp}\) can be regarded as the extra degree-of-freedom to stabilize the cross-tracking error [5]. To prescribe the desired yaw angle \(\psi_{d}\) for \(\psi\), the following form of the guidance law is taken  
\[\psi_{d}=\gamma_{p}-\beta_{r}+\text{atan2}\left(-\left(y_{e}+\alpha_{y}\right), \Delta\right) \tag{11}\]  
where \(\beta_{r}=\text{atan2}\left(v_{r},u_{r}\right)\); \(\Delta\) is the specific look-ahead distance; and \(\alpha_{y}\) is the virtual control input. It should be noted that \(\alpha_{y}\) is introduced to shape the dynamics of the ILOS guidance by inherently adding an integral action.
# Continuous-Wave THz Imaging for Biomedical Samples
## 3 CW THz Full-Field Imaging
### CW THz Full-Field Amplitude Imaging  
In 2017, [PERSON] et al. achieved full-field THz imaging based on a self-developed uncooled supramolecular array detector (128 \(\times\) 128 pixels) for fresh spiders and dehydrated mint leaves [57]. The main and branch vein of the leaf could be clearly identified from the THz images. The fresh spider sample had a higher water content, so the THz radiation could not penetrate it completely, but the image showed some detailed features clearly, including its eight legs and tail. In 2019, [PERSON] et al. presented a THz transmission amplitude imaging setup based on a microresonator frequency comb (microcomb) with a frequency of 331 GHz and a THz focus-plane array camera (32 \(\times\) 32 pixels) [58]. Two peanuts were placed \(\approx\)135 mm away from the detector with a spatial resolution of 1.5 mm. The two peanuts had different structures, one shell containing two nuts and the other containing one nut. The THz images showed that the intensity distribution was lower, which was caused by the fresh nuts having strong absorption of THz radiation.
## 3 System Principle and Realization
The expression of \(MSE\) is as follows:  
\[MSE=\frac{\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{M}\left(f_{ij}-f_{ij}t\right)^ {2}}{M\times N} \tag{2}\]  
Where \(f_{ij}\) and \(f_{ij}\)' represent the original image and the restored image, respectively, and \(1\leq i\leq M\), \(1\leq j\)\(\leq M\). The expression of \(PSNR\) is as follows:  
\[PSNR=10\text{g}\frac{255\times 255}{MSE} \tag{3}\]  
Figure 6 shows the optical signal transmission model of our underwater optical communication system. Emission power rate of LED is 1.1 W, and the emission angle of the collimator is \(5^{\circ}\). The formula of receiving power for underwater optical communication is as follows [27]:  
\[P_{r}=P_{t}\cdot\exp(-c\cdot L) \tag{4}\]  
where \(c\) is the attenuation coefficient, \(P_{t}\) is transmitting power, and \(L\) is the longest communication distance.
NASA's Advanced Rapid Imaging and Analysis (ARIA) project provides Sentinel-1 InSAR beta-products, including geocoded unwrapped interferograms [25] and the post-processing data preparation tools [26; 27] for the GIAnT [18; 19] and MintPy [20; 21] time series analysis packages. As of November 2019, ~45,000 ARIA interferograms are available, mostly limited to parts of the United States of America and China. Looking inside the Continents from Space (LiCS) is one of the projects led by the United Kingdom Natural Environment Research Council's Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET). The aims of LiCS are to monitor global tectonic and volcanic zones using Sentinel-1 InSAR, to map tectonic strain at high spatial resolution, and to use the results to inform new models of seismic and volcanic hazards. LiCSAR, which is an automated Sentinel-1 InSAR processor that uses GAMMA SAR and Interferometry software [28; 29], has been developed to meet the primary LiCS project goals. As of November 2019, ~160,000 interferograms have been produced, mainly covering the Alpine Himalayan Belt and global volcanoes, and are published on the COMET-LiCS web portal [30]. LiCS coverage is currently being expanded to a global scale for deforming regions. We have recently developed an open-source InSAR time series analysis package (LiCSBAS) that integrates with LiCSAR [31]. LiCSBAS utilizes the LiCSAR products so that users do not need to produce interferograms from SLC data. Instead, LiCSBAS enables users to easily obtain InSAR time series and velocity estimates wherever sufficient LiCSAR products are available. These results can be used for a variety of purposes, ranging from large-scale tectonic to localized anthropogenic surface deformation studies. This paper is organized as follows. Section 2 describes the LiCSAR products and LiCSBAS workflow. Sections 3 and 4 present Japan-focused case studies for large- and small-scale deformations,including an evaluation of the time series and velocity uncertainties. In Section 5, we discuss the advantages and limitations of LiCSBAS prior to presenting some concluding remarks in Section 6.
## 3 The Design of GAF MIMO Antenna Array  
### The Structure and Dimension of GAF Antenna Element  
The proposed GAF MIMO antenna array is microstrip antenna with the advantages of low profile, miniaturization and easy integration. Figure 2a is the schematic diagram of the GAF antenna element geometry structure. The GAF antenna is designed on FR-4 PCB substrate with relative permittivity of 4.3 and dielectric loss tangent of 0.03. The thickness (\(h\)) of PCB is 1.6 mm. Unlike traditional antennas, the radiator material of the antenna is highly conductive GAF. The length and width of the dielectric substrate and the radiator are \(L\) = 60 mm, \(W\) = 50 mm, \(L1\) = 19.8 mm, \(W1\) = 24.5 mm, respectively. A 50 ohms microstrip feed line with width of 2.98 mm is utilized to feed for the GAF antenna. The antenna patch and microstrip line can achieve impedance matching by changing the insertion depth (\(g\)). The optimized \(g\) is 5.65 mm.  
The GAF antenna element model is simulated in CST electromagnetic simulation software to analyze the performance. Figure 2b depicts the simulated return loss of GAF  
Figure 2: (a) The schematic diagram of the GAF antenna element geometry structure, (b) the simulated reflection coefficient of GAF antenna element, (c) 3D radiation pattern of GAF antenna element.  
antenna element. The GAF antenna element operates at 3.5 GHz, which is the Sub-6 GHz frequency band of 5G mobile communications. The \(-\)10 dB impedance bandwidth is 3.42-3.57 GHz, which indicates that above 90% energy in this frequency band is radiated to free space via the antenna. The 3D radiation pattern of GAF antenna element is shown in Figure 2c. The simulated realized gain is 2.87 dBi.
## 8 A Critical Discussion on the Techniques of Characterization
### How to Probe the Presence of Nanoparticles by FT-IR  
We have shown how functionalization of inorganic nanoparticles is an emerging way to improve nanoparticle dispersion within the bituminous matrix. Such expensive methods require a technique to easily, and possibly cheaply, observe if the surface functionalization has taken place and to what extent.  
Usually FT-IR has been used for this purpose. For example, the antioxidant effect of the two inorganic nanoparticles was deepened by FTIR by [PERSON] et al. [55]. However, also [PERSON] et al. [59], [PERSON] et al. [64], [PERSON] et al. [62] cited in the present work, have used FT-IR spectroscopy, as it can be seen by perusal of Table 3, which gives a panoramic view of the techniques used for investigating the properties of nanoparticles containing bitumens and asphalts.  
Some comments are now due. Taking the work by [PERSON] et al. [93] as a representative example, the authors, suspecting an irregular dispersion of the nanoparticles in binder, carried out a FT-IR investigation. In their work, the IR peak shifts were individuated as indicators of the coupling agent binding to the nanoparticle surface. We reproduce their key figure in Figure 5  
In their work, tiny shifts of the peaks were interpreted as the consequence that asphalt is subjected to no obvious changes during modification. This was interpreted as that modification of asphalt with Al\({}_{2}\)O\({}_{3}\) nanoparticles is merely a physical process.  
We want to take inspiration from this conclusion to warn that in infra-red spectroscopy even small shifts in the absorption peaks can reveal interesting changes in the vibrational states of the functional groups. This can actually unveil real changes in interactions, which can be effectively treated through proper analysis [124]. An effective analysis must pass, in our opinion, through the separate analysis of the various IR ranges in which the functional groups absorb [125].  
For example, it has been seen that the frequency of C-H stretching are usually quite robust but the relative intensities of the CH\({}_{2}\) and CH\({}_{3}\) symmetric and antisymmetric contributions are not, so they are usually indicated as interesting indicators of the chain packing [11; 126].
## 3 KMDS Method
### Flow Chart and Specific Steps of the KMDS Method  
The flowchart of the KMDS method is shown in Figure 3, and the specific process is completed in the following seven steps.  
**Step 1: Initial experimental design.** Set the sets \(\mathbf{X},\mathbf{X}_{1}\) and \(\mathbf{Y}\) to be empty sets. The \(N\) initial sample points \(\mathbf{x}_{i}(i=1,\cdots,N)\) are generated using the Latin hypercube sampling (LHS) method, and they are added to the set \(\mathbf{X}\). Estimate the expensive function of the initial sample points to obtain \(\mathbf{y}_{i}(i=1,\cdots,N)\), which is added to the set \(\mathbf{Y}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Griewank (50D)** & **KMDS-1** & **KMDS-2** & **KMDS-3** & **KMDS-4** & **KMDS-5** \\ \hline
110 & \(E\) & 0.6818 & 0.5288 & 0.4315 & 0.3601 & 0.3008 \\
220 & \(E\) & 0.7148 & 0.5711 & 0.4753 & 0.4055 & 0.3509 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The distance error between the original data and the low-dimensional data.  
**Step 2: Dimensionality reduction.** The multidimensional scaling method is used to reduce the dimension of data set \(\mathbf{X}\). Section 3.1 shows that the ideal low-dimensional space dimension \(d^{\prime}\) after dimensionality reduction is 3; that is, the original high-dimensional data are reduced to three-dimensional data. See Section 3.1 for the specific dimensionality reduction process. The low-dimensional data are stored in the set \(\mathbf{X}_{1}\).  
**Step 3: Build the Kriging model.** The Kriging model is constructed by using new sample data sets \(\mathbf{X}_{1}\) and \(\mathbf{Y}\). At this time, the Kriging model is three-dimensional.  
**Step 4: Obtain a new sample point.** Obtain a new sample point \(\mathbf{x}_{i}\) through the LHS, and calculate its expensive function value \(\mathbf{y}_{i}\).  
**Step 5: Determine whether the stopping criterion is met.** In this work, the estimated number of sampling points is used as the stop criterion, and the maximum number of evaluations is set to \(N_{max}\). If the number of evaluations is less than \(N_{max}\), proceed to Step 6; if the number of evaluations is greater than or equal to \(N_{max}\), skip to Step 7.  
**Step 6: Update the data set.** The new sample point \(\mathbf{x}_{i}\) is added to the sample set \(\mathbf{X}\), and \(\mathbf{y}_{i}\) is added to the sample set \(\mathbf{Y}\). Go back to Step 2.  
**Step 7: End.** Output the Kriging model.
# SMS: A Secure Healthcare Model for Smart Cities
## 4 Proposed SMS Framework  
### Overview and Architecture  
The proposed framework for the SMS facilitates the round-the-clock monitoring of patients by the doctors, care providers, and hospitals using smart-wearable devices and sensors. The SMS consists of five modules, namely users, a central monitoring unit, encryption and standardization unit, edge nodes, and cloud storage and processing unit. Figure 1 shows the core components in the SMS framework and their interconnection.  
In the proposed framework for the SMS, several hospitals are connected together to facilitate information sharing using blockchain-based information storage. The stakeholders (users, doctors, patients, administrators, care providers) can perform their respective activities by authenticating themselves using the legitimate credentials. Any new user can register in the SMS by providing the respective registration details. Activities like reports downloads, viewing test results, and prescriptions providing diagnosis and suggesting treatments can be done with the help of smart devices using basic internet facilities. As the records of the patients are maintained on blockchains, the possibility of data tampering is negligible. A smart contract consisting of sets of rules, preferred communication modes, and access rights to be followed by the entities of the SMS enables a secured and privacy-preserved information exchange and transfer. The data/requests that require immediate processing are handled on the edge of the network itself by the edge nodes. In order to overcome the security vulnerabilities of the mobile edge computing framework, the notion of the \"OAuth 2.0\" [61; 62] and \"Towfish cryptographic technique\" [63; 64] has been used in the proposed SMS system. The OAuth 2.0 protocol is used for authentication of the users and provides time-based access to the system. Once the time period is expired, access to the system is revoked. The \"Towfish cryptographic technique\" is used to encrypt the data captured by the IoT devices. As Twofish uses a Feistel network and symmetric key approach and is a lightweight algorithm, it is very easy to implement on the IoT devices.
# Modeling Evacuees' Intended Responses to a Phased Hurricane Evacuation Order
## 2 Literature Review and Hypotheses
### Factors Affecting Warning Responses
#### 2.2.2 Initial Evacuation Decisions  
Initial evacuation decisions refer to decisions evacuees make before knowing about the phased evacuation order. A phased evacuation order designates departure times by area. It is likely that not everyone living in the same area finds the designated departure time suits them or their initial evacuation decisions, which could lead to noncompliance behavior. For example, studies about modeling departure-time choice found that: (1) greater risk motivates early departure [48; 49; 50; 51; 52; 53]; and (2) resource affluence, such as higher income [54; 51] and vehicle ownership [55; 51], also leads to early departure.  
**Hypothesis 4 (H4):**_individuals who evacuate to the home of a friend/relative are more likely to comply with a phased evacuation order._  
The home of a friend/relative provides more convenience in terms of flexibility to evacuees compared to hotels/motels or shelters. Specifically, (1) the home of a friend/relative does not have a strict capacity limitation and (2) the home of a friend/relative does not have check-in or opening hours. This flexibility allows individuals who choose the home of a friend/relative to evacuate at different times.  
**Hypothesis 5 (H5):**_individuals who plan to bring more personal vehicles during evacuation are less likely to comply with a phased evacuation order._  
Individuals bringing more vehicles are more likely to evacuate with more household members. Evacuating at an officially designated time seems less convenient than departing at a time when most or all household members are ready.
## 4 Experimental Results and Discussion
### Hyperparameters Search
#### 4.1.3 Effect of the Smoothing Filter  
The effect of the Gaussian smoothing on the estimation of curvatures was investigated. Figure 7 illustrates the maximal principal curvature direction of a mesh partition in colors.  
Hue changes with respect to the azimuthal angle of the curvature direction, while saturation reduces as its elevation angle increases. When the standard deviation of the filter, \(\sigma\), is 0.9 and 1.6 (Figure 7d,e), the vertices are realigned such that surfaces with similar principal directions have the same color. At \(\sigma=4\), the principal direction of the vertices are seriously disoriented, and this standard deviation value is not recommended to be used (Figure 7g).  
\begin{table}
\begin{tabular}{c c|c c c c} \hline
**Trees (\(N_{t}\))** & **Max. Depth (\(N_{d}\))** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
200 & 16 & 0.9548 & 0.8755 & 0.8628 & 0.8691 \\
200 & 32 & 0.9560 & 0.8808 & 0.8636 & 0.8721 \\
200 & 64 & 0.9559 & 0.8811 & 0.8631 & 0.8720 \\
500 & 16 & 0.9549 & 0.8755 & 0.8632 & 0.8693 \\
500 & 32 & 0.9562 & 0.8810 & 0.8651 & 0.8730 \\
500 & 64 & 0.9562 & 0.8814 & 0.8646 & 0.8729 \\
1000 & 16 & 0.9549 & 0.8756 & 0.8635 & 0.8695 \\
1000 & 32 & 0.9562 & 0.8808 & 0.8655 & 0.8731 \\
1000 & 64 & 0.9562 & 0.8813 & 0.8642 & 0.8728 \\
1500 & 16 & 0.9550 & 0.8756 & 0.8637 & 0.8696 \\
1500 & 32 & 0.9562 & 0.8809 & 0.8649 & 0.8728 \\
1500 & 64 & 0.9561 & 0.8812 & 0.8642 & 0.8726 \\ \hline \end{tabular}
\end{table}
Table 1: Search for suitable number of trees and maximum depth for RF.  
Figure 6: The performance of SI-based RF models for different SI parameter combinations (\(\Delta\rho\) and \(\Delta\gamma\)). Both \(N_{\rho}\) and \(N_{\gamma}\) were set to 10. Each RF model was trained with 500 trees and maximum depth of 32. The best SI parameter combination is \((\Delta\rho,\Delta\gamma)=(0.60,0.50)\).  
Since the estimation of curvatures is directly linked to the localization of the four relative points used in CSI, it is also vital to demonstrate how the variation in \(\sigma\) affects the extraction of reliefs. For \(s=2\), the size of the subsampled SI is \(5\times 5\), and \(d_{CSI}\) is initially selected as 1. Figure 8 shows that, as the value of \(\sigma\) increases from 0, the performances of the models also increase up to the highest F1 score of 0.8828. However, after \(\sigma>1.6\), the performances of the RF models start to decline. This observation is in line with the colormap of curvature directions reported in Figure 7, in which, when \(\sigma\) is large, the surface becomes severely flattened and the curvature directions does not provide any useful information. In the proposed method, \(\sigma=1.6\) was used for the Gaussian filter before estimating the principal curvatures of the mesh surface.
###### Abstract  
This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m\({}^{2}\)), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.  
solar power plant; Samcheonpo; meteorological data; big data; regression model
## 2 Background
### Exponentially Weighted Moving Average Model
Each factor is expressed as  
\[\hat{Y}_{t}=\left\{\begin{array}{c}\alpha Y_{t}+(1-\alpha)(\hat{Y}_{t-1}+ \Delta\hat{Y}_{t})\ if\ t<L\\ \alpha(  
However, it is not easy to estimate such parameters accurately when the historical data are not sufficient or if prior knowledge on the time series is not available. Thus, the triple ES model might not be an appropriate base model for the self-starting forecasting process under insufficient initial historical observations. In addition, these traditional EWMA models are based on the assumption that the future observations will have a similar pattern to past observations. However, in various fields, such as signal processing, the time-varying patterns of future observations are unknown and far from the historical pattern [29; 30]. Consequently, these traditional models often suffer from the bias of forecasting results from true future observations because of this assumption.  
To address this drawback of the traditional EWMA models, [PERSON] and [PERSON] proposed two-stage EWMA, which mimics the double ES model [29; 30]. The two-stage EWMA can be formulated with three factors: level factor, adjustment factor, and drift factor. In the first step, the level factor at time \(t\), \(\hat{Y}_{t}\), can be computed as follows:  
\[\hat{Y}_{t}=\alpha Y_{t}+(1-\alpha)\hat{Y}_{t-1}, \tag{11}\]  
where \(\hat{Y}_{1}=Y_{1}\). Then, the adjustment factor at time \(t\), \(d\hat{Y}_{t}\), can be estimated as follows:  
\[d\hat{Y}_{t}=\beta dY_{t}+(1-\beta)d\hat{Y}_{t-1} \tag{12}\]  
where \(dY_{t}\) is a first-order difference between time t and t-1, which can be defined as follows:  
\[dY_{t}=Y_{t}-Y_{t-1}. \tag{13}\]  
In addition, \(d\hat{Y}_{2}=Y_{2}-Y_{1}\). Finally, we estimate the drift factors of time t by  
\[\Delta\hat{Y}_{t}=rdY_{t}+(1-r)\Delta\hat{Y}_{t-1}, \tag{14}\]  
where \(\Delta\hat{Y}_{2}=\ Y_{2}-Y_{1}\). The final forecast for \(s\) further times from the current time \(t\) can be computed by the following equation:  
\[\hat{Y}_{t}=\sum_{i=0}^{t}\alpha(1-\alpha)^{i}Y_{t-i} \tag{2}\]0  
The drift factor plays similar role to the trend factor in the double ES model. However, it should be noted that the drift factor is defined as a difference between two successive observations, and hence, the drift factor can more accurately reflect the dynamically changing patterns than the trend factors, which is the difference between the estimated level factors of two successive time points. Moreover, this method involves the adjusted factors in order to alleviate the effects from sudden and unexpected changing patterns. Hence, the two-stage EWMA method can outperform the double ES method.