esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Benchmark Examples with Analytic Solution
### Example--First-Order System  
Consider a scalar nonlinear system described by the following equation [21]:  
\[\dot{x}=u+\text{arctg}(x)\cdot d,\quad\mathbf{z}=\begin{bmatrix}x\\ u\end{bmatrix}. \tag{40}\]  
If \(\gamma>\frac{\pi}{2}\), then by solving the corresponding HJI equation (for details see [21]), the analytical feedbacks are  
\[\begin{split} u^{*}&=-x\bigg{(}1-\frac{1}{\gamma^{2}}\text{ arctg}^{2}(x)\bigg{)}^{-\frac{1}{2}},\\ \\ d^{*}&=\frac{1}{\gamma^{2}}x\,\text{ arctg}(x)\bigg{(}1-\frac{1}{\gamma^{2}}\text{ arctg}^{2}(x)\bigg{)}^{-\frac{1}{2}}.\end{split} \tag{41}\]  
Approximation functions of control and uncertainty variables are chosen in the following form  
\[\begin{split}\dot{u}(x)&=p_{1}x+p_{2}x^{3}+p_{3}x^{5}+p_{4 }x^{7}+p_{5}x^{9},\\ \dot{d}(x)&=r_{1}x+r_{2}x^{3}+r_{3}x^{5}+r_{4}x^{7}+r_{5}x^{9 },\end{split} \tag{42}\]  
i.e., written in the form (12) and (13) we have  
\[\mathbf{\Theta}(x)=\mathbf{\Psi}(x)=\begin{bmatrix}x&x^{3}&x^{5}&x^{7}&x^{9}\end{bmatrix},\ \ \ \mathbf{\pi}=\begin{bmatrix}p_{1}\\ p_{2}\\ p_{3}\\ p_{4}\\ p_{5}\end{bmatrix},\ \ \mathbf{\rho}=\begin{bmatrix}r_{1}\\ r_{2}\\ r_{3}\\ r_{4}\\ r_{5}\end{bmatrix}. \tag{43}\]  
In this example, we set the following values of the algorithm parameters: we divided the time interval from \(t_{0}=0\), to \(t_{f}=10\) [s] into \(N=\)100,000 equal subintervals, i.e., the discretization step is \(\tau=10^{-4}\) [s] and we applied the Adams method of the 4-th order; vector of initial conditions \(\mathbf{x}_{0}=[1]\); initial value of the parameter \(\mu_{0}=\gamma_{0}^{2}=5\); vectors of initial parameters of approximation functions \(\pi_{0}=\rho_{0}=\mathbf{0}\); stopping criterion of the quasi-Newton method \(\varepsilon=10^{-3}\); stopping criterion of conjugate-gradient method \(\varepsilon=10^{-6}\); Dai-Yuan method is used by default, the initial numerical values of the conjugate gradient algorithm parameters are chosen as \(\eta_{0}=0.1\) and \(\beta_{0}=0.5\), coefficients \(c_{1}=10^{-3}\), \(c_{2}=0.9\) and \(\
u=0.8\).  
We obtain the following parameter \(\mu\) and parameters of approximation functions:  
\[\mu^{*}=2.4667,\quad\mathbf{\pi}^{*}=\begin{bmatrix}-0.9999\\ -0.2045\\ 0.0781\\ -0.0391\\ 0.0112\end{bmatrix},\quad\mathbf{\rho}^{*}=\begin{bmatrix}0.0414\\ 0.9638\\ -1.7517\\ 1.8462\\ -0.7361\end{bmatrix}, \tag{44}\]  
i.e., minimum \(\mathcal{L}_{2}\)-gain is \(\gamma^{*}=\sqrt{\mu^{*}}=1.5706\).
# Reliable Internet of Things: Challenges and Future Trends
## 3 Major Use Cases of IoT
### Industrial Internet of Things  
Industrial Internet of Things (IIoT) is an emerging use case of IoT. By connecting machines with the Internet, their health status can be regularly monitored, and repairs can be planned, thus reducing the outages. Moreover, many industrial processes can be automated and smartly controlled to increase productivity [63; 64; 65; 66; 67; 68; 69; 70].  
The sensed data in IIoT can be electrical current in a transistor, wear and tear of the machine and operating conditions of the industrial process. In addition, the productivity data of the industrial process and machine output can be sent to a cloud server [71; 72; 73; 74].  
The data dissemination in IIoT is generally handled using cellular communications. In scenarios where the power of the sensors is an issue, short-range and low-powered communication such as Zigbee can be used as first-hop [75; 76].  
Based on the received data, IIoT can provide predictive maintenance. In addition, analyzing the past values of the various parameters, changes in the operating conditions of the industrial processes can be made. In addition, the trend of the production values can indicate if any change is required to achieve the optimal yield of the industrial process.
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
## 1 Introduction
The performance validation of the proposed controller was conducted not only through dynamic simulations, but also through wind tunnel experiments in turbulent wind conditions with sensor noise and blade fault situations using a scaled wind turbine with a capacity of 40 W.  
The contribution of this study can be summarized as follows:  
1. To consider the nonlinearity of wind turbines caused by wind disturbances such as turbulence and gusts, membership functions and fuzzy rules were designed for wind speed information and integrated into \(H_{\infty}\) control;  
2. To cope with the model uncertainty of wind turbines, such as sensor noise and the perturbation that causes control performance degradation, we designed a \(H_{\infty}\) control algorithm based on a mixed-sensitivity method was designed;  
3. To validate the proposed algorithm more clearly, the control performances have been compared with those of previously proposed algorithms, including the fuzzy logic-based LQR and pitch-\(H_{\infty}\) control, as well as the baseline PI control algorithm;  
4. To show the feasibility of the proposed control algorithm in close-to-real situations, the proposed control algorithm was experimentally validated using a 40 W capacity-scaled wind turbine and turbulent winds in a wind tunnel. Also, the failure situation of a blade-pitch actuator was tested and analyzed.
## 6 Discussion
### Advanced Intelligent Closed-Loop Neural Control Strategies  
Digital PID Control MIMO CCS MISO ANFIS Models  
The MATLAB Simulink simulation results of the digital PID control CCS ANFIS model without changes in temperature and level setpoints, depicted in Figure 13a,b, respectively, show a fast time response Ts = 50 s from the evaporator temperature control subsystem, with zero steady-state error and a 9% temperature of chilled water overshoot, compared with the liquid refrigerant level control inside the condenser subsystem which had Ts = 64 s, steady-state error, and 2% overshoot of the liquid refrigerant level in the condenser. Moreover, the actuator control efforts for the compressor and expansion valve opening, correspondingly shown in Figure 14a,b, are sharp at the beginning and smooth later. The  
Figure 19: Statistical data extracted from the time responses: (**a**) PID evaporator temperature control; (**b**) PID condenser liquid refrigerant level control.  
Simulink simulation results depicted in Figure A4a,b reveals that the effects of changes in disturbance temperature load Trr from 48 to 54 degrees Fahrenheit applied at time t = 70 s were completely overcome via the chilled temperature control inside the evaporator, as were those in the level of liquid refrigerant within the condenser, almost within 40 s. Also, the figures indicate that the steady state for each controlled output was very close to zero, and the settling times of the step responses were fast enough for both digital PID controllers, which can be interpreted as excellent performance using the improved digital PID control strategy, outperforming the previous control PID control strategy based on the extended model developed in Section 3.2. For this reason, the digital PID with the proposed tuning procedure for the parameters' values was used for performance comparison with the second RL DLNN control structure.
# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
## 4 Discussion and Conclusions
We may conclude that the geometrical properties improved the classification performance at higher magnifications. In our future work, more advanced image processing techniques will be considered at the preprocessing stage, which can help accentuate the geometrical properties of each individual cell. The final decision-making method considering the distribution of patch-wise classification results can be one of our future works. Conceptualization, H.L., G.L., and A.H.; methodology, H.L., J.S., and D.Y. ; visualization, H.L. and G.L. ; writing--original draft, H.L. ; writing--review and editing, D.Y. and A.H.; funding acquisition, J.P.; supervision, D.Y. and A.H. All authors have read and agreed to the published version of the manuscript. This work was supported by the grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number: HI19C0642), and a National Research Council of Science and Technology (NST) grant from the Korean government (MSIP) (No. CRC-15-05-ETRI). De-identified pathology images and annotations used in this research were prepared and provided by the Seoul National University Hospital by a grant of the Korea Health Technology R&D Project through the KHIDI, funded by the Ministry of Health and Welfare, Republic of Korea (grant number: HI18C0316). Not applicable. Restrictions apply to the availability of these data. Data was obtained from PAIP 2020 challenge and are available [[https://paip2020.grand-challenge.org/Home/](https://paip2020.grand-challenge.org/Home/)]([https://paip2020.grand-challenge.org/Home/](https://paip2020.grand-challenge.org/Home/)) with the permission. The authors declare no conflict of interest.
# Assessment of Hongtu-1 Multi-Static X-Band SAR Constellation Interferometry
## 4 Discussion
The HT1 X-band multi-static coherence product showed good potential to visually discriminate important landcover classes such as forest, open areas, urban areas and water. It differs from previously used coherence products in that it uses data acquired on a single date.
## 2 Numerical Simulation Scheme  
### Establishing the Numerical Model  
Using discrete element 3 DEC software (3.0 version), a numerical model of the SBRS of the same size was created based on our earlier shaking table test [20] (Figures 1 and 2). The dynamic response and damage mechanisms of the slope under seismic action were thus analyzed through the extension of numerical simulation. The slope model's precise dimensions are 140 cm for length, 83 cm for width, and 130 cm for height. Its slope angle is 50\({}^{\circ}\), its horizontal distance from the rock layer is 13 cm, its dip angle is 55\({}^{\circ}\), and a mica sheet is placed in between the layers. It should be noted that when using numerical simulation for seismic conditions, in order to meet the simulation accuracy requirements, the mesh size \(\Delta l\) follows the rule proposed by [PERSON] and [PERSON] [28], which is estimated as follows:  
\[\Delta l\leq\frac{\lambda}{10}\ \sim\ \frac{\lambda}{8}, \tag{1}\]  
where \(\Delta l\) is the mesh size, and \(\lambda\) is the wavelength corresponding to the highest component frequency of the input wave. Accordingly, we set the mesh size to 0.05 m to ensure the complete propagation of seismic waves in the medium, and the final delineation model contains a total of 13,723 grid nodes and 50,198 mesh elements (Figure 2).  
Figure 1: The steep bedding rock slope model. (unit mm).  
The Mohr-Coulomb yield criteria was employed for analysis, and the plastic constitutive model was used for the rock mass in the numerical model of the slope. Table 1 displays the physical and mechanical characteristics. The Coulomb-slip yield criterion was used for the weak layer, and the empirical Formulas (2)-(4) and the analogy approach were used to calculate its parameters. The specific values are shown in Table 1.  
\[k_{n}=k_{s}=10\times\left[\text{max}\Bigg{(}\frac{K+\frac{4}{3}G}{\Delta Z_{ \text{min}}}\Bigg{)}\right], \tag{2}\]  
\[K=\frac{E}{3(1-2\mu)}, \tag{3}\]  
\[G=\frac{E}{2(1+\mu)}, \tag{4}\]  
where the normal and tangential stiffness of a joint (weak layer) are denoted by \(k_{n}\) and \(k_{S}\), \(\Delta Z_{min}\) is the minimum size of the neighboring mesh in the normal direction, and \(K\) and \(G\) are the bulk modulus and shear modulus of the rock mass.
## 3 Results and Discussion
### ASE&C Technology Competitive Advantages
Although some of them have lower CAPEX values, they also include several environmental concerns (especially brine discharges). RO and ion exchange units are commonly used in lithium recovery from geothermal brines [34]. Direct lithium extraction employs significant volumes of water and chemicals, has high energy consumption, and generates wastes [35]. In contrast, other evaporitic technologies employed in brine mining (such as lithium extraction) have intensive water use, protracted duration, and particular use over continental brines [35]. Ultrafiltration combined with RO and/or nanofiltration have recently been used [36]. Some other zero-discharge desalinations use hybrid desalination with RO and nanofiltration or electrodialysis, metathesis and evaporation ponds, with significant recovery of water (97%) and fractions of multivalent ions (60%), but also with substantial energy consumption (0.77 kWh per g TDS/L\({}^{-1}\)) [37] and potentially detrimental effects due to the costly use of membranes and chemicals. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **RO** & **Evaporation** & **Crystallization** &
\begin{tabular}{c} **Evap + Cryst** \\ **2 Steps** \\ \end{tabular} & **ASEC** \\ \hline Water/solid & 45\% & 80\% & 80–100\% & 90–100\% & 100\% \\ separation & 3–5 & 80 & 250–500 & 130–230 & 0–20 * \\ (kWh/m\({}^{3}\)) & & & & & \\ \hline Use of & & & & & \\ chemicals & Yes & Yes & Yes & No \\ \hline CAPEX & & & & & \\ (\(\ell\)/m\({}^{3}\)) & 700–2000 & 15,000 & 20,000–40,000 & 30,000 & 5000–15,000 \\ Environmental & & & & & \\ concerns & & & & & \\ discharges & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ASE&C vs. other competing technologies (reverse osmosis: RO, evaporation and crystallization).
###### Abstract  
The densification of multiple wireless communication systems that coexist nowadays, as well as the 5G new generation cellular systems advent towards the millimeter wave (mmWave) frequency range, give rise to complex context-aware scenarios with high-node density heterogeneous networks. In this work, a radiofrequency electromagnetic field (RF-EMF) exposure assessment from an empirical and modeling approach for a large, complex indoor setting with high node density and traffic is presented. For that purpose, an intensive and comprehensive in-depth RF-EMF E-field characterization study is provided in a public library study case, considering dense personal mobile communications (5G FR2 @28 GHz) and wireless 802.11 ay (@60 GHz) data access services on the mmWave frequency range. By means of an enhanced in-house deterministic 3D ray launching (3D-RL) simulation tool for RF-EMF exposure assessment, different complex heterogenous scenarios of high complexity are assessed in realistic operation conditions, considering different user distributions and densities. The use of directive antennas and MIMO beamforming techniques, as well as all the corresponding features in terms of radio wave propagation, such as the body shielding effect, dispersive material properties of obstacles, the impact of the distribution of scatterers and the associated electromagnetic propagation phenomena, are considered for simulation. Discussion regarding the contribution and impact of the coexistence of multiple heterogeneous networks and services is presented, verifying compliance with the current established international regulation limits with exposure levels far below the aforementioned limits. Finally, the proposed simulation technique is validated with a complete empirical campaign of measurements, showing good agreement. In consequence, the obtained datasets and simulation estimations, along with the proposed RF-EMF simulation tool, could be a reference approach for the design, deployment and exposure assessment of the current and future wireless communication technologies on the mmWave spectrum, where massive high-node density heterogeneous networks are expected.  
radiofrequency electromagnetic fields (RF-EMF); mmWave; electromagnetic safety; 3D ray launching (3D-RL); 5G; 802.11 ay; propagation modeling +
Footnote †: journal: Sensors
## 8 Results and Discussion  
### Simulation Set Up  
The introduced IoT-based healthcare model via HEM-AL + DCNN was implemented with **MATLAB**. The implementation was performed with respect to \"accuracy, sensitivity, specificity, precision, F1-score, Negative Predictive Value (NPV), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), False Discovery Rate (FDR) and False Positive Rate (FPR)\" by varying the test cases from 1, 2 and 3 (three diseases). Consequently,the enhancement of the developed HEM-AL + DCNN model was measured by evaluating it over extant schemes, namely MFO + DCNN [33], EHO+ DCNN [32], While Optimization Algorithm (WOA) + DCNN [37], and EEPSO + DCNN [21] models. Here, convergence analysis was computed for different iterations numbering 0, 5, 10, 15, 20 and 25.
## 2 Related Work  
Several studies have developed graphical user interface software tools in the technical field of telecommunications for further advancement in telecommunication and networks.  
The work in [5] presents an application that uses algorithms for signal diffraction and reflection calculation. This simulation program is based on the updated recommendations of the International Telecommunication Union (ITU-R) radiocommunication sector. This simulation program was developed with Adobe Xd software, and the simulation results are validated through comparison with other professional tools.  
The authors of [6] analyze the path loss prediction in an indoor environment at a frequency of 433 MHz. In this survey, reflection coefficient models are used to optimize the path loss prediction of the propagating signal. This tool is designed using MATLAB, and the results indicated that antenna position is important for channel modeling.  
The authors of [7] study a successful wireless link calculation designed using MATLAB. This MATLAB code was developed to compute and analyze the link budget. This software tool explores all the parameters, gains, and losses that may arise between a transmitter and receiver applied in satellite communication.  
Research published in [8] presents a mobile application developed in MATLAB/GUI to help students understand the basic principles of communication engineering. The results show that the values obtained from a mobile are reliable after comparing them to the calculated values.  
Paper [9] shows the graphical user interface developed using MATLAB. The tool describes the link budget scheme for power-based private wireless data networks and performs an estimation of the coverage and area of a single cell. The estimated results are the same as the existing measurement results.  
Enriching the already existing research, this study focuses on the design and calculation of transmission parameters in a point-to-point wireless Ethernet bridge connection. It is also worth mentioning that developing a GUI environment using the Python programming language and the Tkinter library is considered to be a better choice due to its widespread use by engineers, scientists, and students for computer-related tasks [10, 11]. In summary, creating scenarios using these features provides a cost-effective solution without the need for special equipment or deep technical programming knowledge.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 4 Results
### Summary of Combined Landsat 9 OLI Results  
A summary of the Landsat 9 OLI results using all data from the three field teams is shown in Figure 18 for the period from November 2021 to June 2023. The results comprise a total of 17 ground-based collects using the RadCaTS and the reflectance-based approach, and the uncertainty bars in this case are the standard deviation (k = 1) of the field results. The results show excellent agreement with both OLI TOA products and are well within the specified radiometric uncertainties of the OLI.  
Figure 17: A summary of the radiometric calibration results for the Landsat 9 OLI from two ground teams at Ivanpah Playa during the tandem flight phase with Landsat 8 (13 November 2021). The results are shown as the ratio of the TOA quantities for each data product (e.g., spectral radiance or reflectance) to those determined using the reflectance-based approach.  
Figure 18: A summary of the radiometric calibration results for the Landsat 9 OLI using all available ground-based data, which include the RadCaTS and the reflectance-based results from three groups (November 2021–June 2023). The results are shown as the ratio of the TOA quantities for each data product (e.g., spectral radiance or reflectance) to the ground-based data, and the uncertainty bars for each band are the standard deviation (k = 1) of the field measurements. The same results in tabular form are presented in Table 4.  
A summary of the temporal results for TOA spectral radiance is shown in Figure 19, which includes all data from each field team. The results do not show a statistically significant trend, but any such potential trend will become apparent once more temporal data are acquired.  
Figure 19: The TOA spectral radiance temporal results for the Landsat 9 OLI, comprising all data from each ground team during the period from November 2021 to June 2023.  
One interesting result from the tandem flight phase with Landsat 8 in November 2021 is observed in both the in situ data collected at Ivanpah by two separate ground teams (Figure 17) and at Railroad Valley using the RadCaTS (Figure 19). The Landsat 9 near-nadir overpass at each of these sites on 13 November 2021 is separated by 45 s, and the data from all three collects show similar results in the ratio of OLI TOA values to the ground measurements. This result is encouraging, as the two systems have a different uncertainty traceability path, and they produce results within the combined uncertainty of each methodology. The results from this single day have a smaller ratio of TOA spectral radiance (OLI/ground) than most of the other dates in this work, but the fact that it occurs at two separate sites with separate traceability paths does increase confidence in the result. The results of this work do not show a significant trend in the radiometric calibration of Landsat 9 OLI, but new data are being routinely collected by the RadCaTS and SDSU for future analysis.