esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Measurement Results
In Figures 10 and 12, the measured gain of antenna arrays with no auxiliary dipole antenna or with two auxiliary dipole antennas is greater than the simulated gain, while in Figure 11, the simulated gain of the antenna array with one auxiliary dipole antenna is greater than the measured gain. This is because the three antenna arrays are fabricated and measured two times--the antenna with one auxiliary dipole antenna is measured first, while the remaining two antennas are measured second time. Additionally, there are errors in measurements at different times. The far-field antenna measurement system mainly consists of a microwave anechoic chamber, a transmitting and receiving turntable, a vector network analyzer, measurement-related connection accessories, instrument control, data acquisition, data processing software and related auxiliary equipment and so on. The amplitude phase synthesis method is mainly used to measure the antenna, which requires the devices of the whole RF link to maintain a stable phase. In this way, according to the collected amplitude and phase information, we can calculate the antenna pattern by algorithm. The test system is carried out according to the comparison method in the antenna standard test method \"ANSI/IEEE Std 149-1979\". The gain of the antenna under test is obtained by comparing the antenna under test with the standard gain antenna. Specifically, the antenna gain is also obtained by dividing the method into two steps. The first step is called calibration, that is to measure the standard antenna; the second step is to measure the antenna under test, and calculate the gain of the antenna under test according to the data obtained from the two measurements. When measuring the standard gain antenna and the antenna under test, ensure that the antenna under test and the transmitting antenna are completely aligned, that is, to align the two antennas manually or automatically to make the measurement results correct. Figure 11: Simulation and measurement results of the (**a**) S parameter, (**b**) gain in the X–Y plane, and (**c**) gain in the X–Z plane of the two-element end-fire antenna array with 1 auxiliary dipole antenna.
# Dual-Band Frequency Selective Surface-Backed Reflectarray for High-Speed Ka-Band Satellites
## 4 Experimental Verification
### Measurement Results  
The dual-band reflectarray is measured with a 40 GHz Anritsu Vector Network Analyzer. The scattering parameters and radiation pattern of the reflectarray system are measured. The measurement of the radiation pattern is carried out in \(\phi=0^{\circ}\) plane within \(0^{\circ}\leq\theta\leq 90^{\circ}\) angular range. An identical double-ridged horn antenna is used as the reference antenna in the measurements. The reflectarray antenna in the measurement setup is shown in Figure 13.  
Figure 10: Normalized radiation patterns of the dual-band FSS-backed reflectarray system in \(\phi=0^{\circ}\) plane.  
The measured reflection coefficient of the dual-band reflectarray is given in Figure 14. The \(S_{11}\) variation in the reflectarray as the function of frequency shows that impedance matching is observed for the frequencies above 18 GHz. This result is expected due to the cutoff frequency of the horn antenna with a WR42 waveguide--2.92 mm connector adapter. When the uplink and downlink frequencies of the Ka-band satellites are considered, we can conclude that the system work properly in terms of impedance matching. Measures normalized radiation patterns of the dual-band reflectarray in \(\phi=0^{\circ}\) plane are given in Figure 15. Co- and cross-polarized patterns are shown at 20 and 30 GHz within \(0^{\circ}\leq\theta\leq 90^{\circ}\) angular range. Compatible with the simulation and array theory results, beam radiation towards \(\theta=20^{\circ}\) is observed in both simulations. The crosspol level is about \(-35\) dB at both 20 and 30 GHz patterns. The maximum gain of the reflectarray is measured as 21.13 dBi and 26.94 dBi at 20 and 30 GHz, respectively. A 3 dB beamwidth of the co-polarized pattern at 20 GHz is observed as \(5.3^{\circ}\), where it reduces to \(3.8^{\circ}\) at 30 GHz. And 3 dB beamwidths of the measured patterns are almost identical to the simulated patterns. The aperture efficiency of 55% and 51% are achieved at 30 and 20 GHz, respectively. The reduction in the efficiency at the upper and lower bands are attributed to the narrowed reflection phase range due to the FSS structure. Measured gain variation in the dual-band reflectarray at Ka-Band satellite downlink and uplink frequencies are given in Figure 16. The blue and pink shaded areas show the downlink and uplink frequency ranges, respectively. The results show that the proposed dual-band FSS-backed reflectarray is suitable for high-speed Ka-band satellites.  
Figure 11: Fabricated printed circuit boards of the reflectarray: (**a**) top side of 30 GHz reflectarray; (**b**) bottom side of 30 GHz reflectarray (FSS surface); (**c**) top side of 20 GHz reflectarray; (**d**) bottom side of 20 GHz reflectarray.  
Figure 12: Prototype of the fabricated reflectarray system and the holder: (**a**) before assembling; (**b**) side view of the assembled prototype.  
Figure 14: Measured reflection coefficient variation in the reflectarray.  
Figure 13: Measurement setup of the reflectarray system.
## 2 Materials and Methods
### Data Acquisition Method  
As the coated bolts are cylindrical, blind spots may occur depending on the location of the cameras. Should the defect be in a blind spot, the system would not be able to provide accurate sorting results. Furthermore, sorting speed decreases if only one camera films the rotating bolt because the image acquisition time increases. Therefore, four cameras were used simultaneously to acquire images. Table 1 shows the camera specifications. A system emulating an actual production conveyor belt was installed as shown in Figure 3; the jigs were manufactured and installed to fix the cameras in place. Additionally, the distance, angle, and viewing angle between the bolts and cameras were adjusted to obtain clear images of the bolt threads.  
Figure 2: Defect classification criteria of coated bolts.  
The images of the coating solution defects presented differently depending on the lighting environment. Bolts with insufficient coating, in particular, displayed different defects in terms of size and location depending on the lighting. Therefore, the circular and straight lighting devices were positioned appropriately to minimize shadow interference for bolts with insufficient coatings. Image-based CNN provides high accuracy for images with a decent resolution; however, higher resolutions result in lower learning speeds. Therefore, we selected a resolution that allowed the clear identification of coating defects with the naked eye. Using the preset criteria and data imaging environment, a total of 652 image data points were obtained, half of which were designated as defective and half as non-defective.
# Symbol Error-Rate Analytical Expressions for a Two-User PD-NOMA System with Square QAM
###### Abstract  
Power domain non-orthogonal multiple access (PD-NOMA) is one of the most perspective multiplexing technologies that allows improving the capacity of actual networks. Unlike orthogonal multiple access (OMA), the PD-NOMA non-orthogonally schedules multiple users in the power domain in the same orthogonal time-spectrum resource segment. Thus, a non-orthogonal multiplexed signal is a combination of several user signals (usually, modulation and coding schemes (MCS) based on quadrature amplitude modulation) with different power weights. The symbol error rate (SER) and bit error rate (BER) performances are one of the main quality characteristics of any commutation channel. The issue is that a known analytical expression for BER and SER calculation for conventional OMA cannot be applied in terms of the PD-NOMA. In the following work, we have derived the SER and BER analytical expressions for gray-coded square quadrature amplitude modulation (QAM) user channels that are transmitted in two-user PD-NOMA channel under additive white Gaussian noise (AWGN). Through the simulation, the verification of the provided expressions is presented for four multiplexing configurations with various user power weights and QAM order combinations.  
NOMA; OMA; PD-NOMA; QAM; BER; SER +
Footnote †: journal: Applied Optics and Photonics
###### Abstract  
Concerns about cybersecurity and attack methods have risen in the information age. Many techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs), that help achieve security goals, such as detecting malicious attacks before they enter the system and classifying them as malicious activities. However, the IDS approaches have shortcomings in misclassifying novel attacks or adapting to emerging environments, affecting their accuracy and increasing false alarms. To solve this problem, researchers have recommended using machine learning approaches as engines for IDSs to increase their efficacy. Machine-learning techniques are supposed to automatically detect the main distinctions between normal and malicious data, even novel attacks, with high accuracy. However, carefully designed adversarial input perturbations during the training or testing phases can significantly affect their predictions and classifications. Adversarial machine learning (AML) poses many cybersecurity threats in numerous sectors that use machine-learning-based classification systems, such as deceiving IDS to misclassify network packets. Thus, this paper presents a survey of adversarial machine-learning strategies and defenses. It starts by highlighting various types of adversarial attacks that can affect the IDS and then presents the defense strategies to decrease or eliminate the influence of these attacks. Finally, the gaps in the existing literature and future research directions are presented.  
adversarial machine learning; intrusion detection systems; adversarial attacks; machine learning; deep learning; network security +
Footnote †: journal:  
0
Footnote 0: _Future Internet_
## 2 Research Methods and Means
### PFC-FLUENT Numerical Simulation of Gob Flow Field
#### 2.2.2 CFD Model and Boundary Conditions  
The gob flow field and gas distribution of the working face were simulated using the ANSYS Fluent software. Fluent is a CFD calculation software used to simulate the fluid flows and heat conductions in complex shapes. It is highly efficient and accurate.  
A simplified 3D grid model of fully mechanized mining gob was established using the basic parameters of the 31,116 working face in Jinjie Coal Mine. First, based on the real size of the working face, a 3D model was constructed using the Solidworks software, with the length, width, and height of the gob set to 500 m, 336 m, and 50 m, respectively. To meet the requirement of the later simulation, structural hexahedral meshing was conducted using the ICEM-CFD software to construct the 3D grid model as shown in Figure 3.  
The mesh quality will directly affect the accuracy and convergence speed of the Fluent numerical simulation. As shown in Figure 4 for the mesh quality examination histogram, the mesh qualities range from 0.981 to 1, meeting the requirements of FLUENT calculation.  
Figure 4: Mesh quality distribution diagram (own elaboration).  
Figure 3: The 3D model and grids of gob in working face (own elaboration).  
Figure 2: PFC model and caving process of the coal seam model (own elaboration). (**a**) Three-dimensional (3D) numerical model diagram. (**b**) Schematic diagram of caving process.  
The inlet of the intake airway of the working face is defined as the velocity inlet, and the boundary condition type is velocity inlet. The outlet of the return airway is defined as the outflow. The two surfaces between the working face and the gob are defined as the interior. The entire 3D model is defined as fluid in ICAM-CFD. The calculation conditions are then initialized to conduct the iterative calculation for numerical simulation.
## 2 Materials and Methods
### Dual-Adaptive PV Panel Architecture  
In this article, a method has been developed to arrange the reconnection structure of the panels in the PV array, which consists of dual-adaptive and fixed parts. The panels of the first and last columns of the PV array in TCT structure and mxn arrangement constitute the adaptive parts, and the remaining part constitutes the fixed part. The panels in the adaptive section can be connected to any row of the fixed section with a switching matrix circuit consisting of electronic switching elements [25]. In order to achieve higher efficiency from the array, the GA-based algorithm developed within the scope of the method decides which row of the fixed part the panels in the adaptive part will be connected to, and the necessary signals are transmitted to this electronic device. This new structure of the PV array is shown in Figure 3. The input parameters of the control system are only the short circuit currents of the panels in the adaptive structure and the rows in the fixed part. Thus, the method can be provided with a minimum number of sensors for implementation. Repetition of the algorithm will start detecting when there is a change in the power value drawn from the PV array. This is due to the power change that occurs when there is an irradiance change on the array or a partial shadow occurs. The reason for measuring short circuit currents is due to the linear relation between the irradiance of PV panels and short circuit currents, as per the equation given below. Thus, for the application of the method, it is not necessary to know the irradiance for the necessary parts of the PV array.  
\[I_{ph}=\frac{G}{G_{n}}(I_{\text{c}cn}+K_{i}(T-T_{n})) \tag{1}\]  
As can be seen from Equation (1), the current (\(I_{\text{ph}}\)) that the PV module will flow is directly proportional to the radiant intensity \(G\) (W/m\({}^{2}\)). Here, \(I_{\text{c}cn}\) shows the nominal short circuit current of the cell, while \(G_{\text{n}}\) and \(T_{\text{n}}\) are the nominal radiant intensity (1000 W/ m\({}^{2}\)) and temperature values (25\({}^{\circ}\)), respectively.  
Figure 2: Most commonly used PV array structures: (**a**) S&P; (**b**) TCT.
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Field of Application** & **RTU Hardware** & **Protocol (RTU to MTU)** & **MTU (Software–Hardware)** \\ \hline
[17] & Plant monitoring & NodeMCU & HTTP & Authors developed web application–Raspberry Pi \\ \hline
[33] & Power distribution system & No specific information provided & 6 LoWPAN & No specific information provided \\ \hline
[34] & Microgrid testbed & Arduino & NRF24L01-SPI & Authors developed Web application–Raspberry Pi \\ \hline
[35] & Photovoltaic (PV) system & Raspberry Pi & HTTP & EmonCMS–Raspberry Pi \\ \hline
[36] & Photovoltaic (PV) system & Raspberry Pi & MQTT & ThingsBoard–Raspberry Pi \\ \hline
[37] & Hydropower system & ESP8266 & HTTP & Authors developed web application–no specific information provided \\ \hline
[38] & Photovoltaic (PV) system & ESP32 & HTTP & Thinger.IO–Raspberry Pi \\ \hline
[39] & Autonomous assembly system & No specific information provided & No specific information provided & No specific information provided \\ \hline
[40] & Wastewater treatment & NodeMCU & HTTP & Authors developed web application–free cloud server \\ \hline
[41] & Base Transceiver Station (BTS) & ESP32 & HTTP & Arduino IoT Cloud–Arduino IoT cloud server \\ \hline
[42] & Tunnel light control & Micro850 Programmable Controller & No specific information provided & No specific information provided \\ \hline
[43] & Education & ESP8266 & HTTP & Authors developed web application \\ \hline
[44] & Oil production & Arduino & HTTP & Node-RED IoT platform–computer server \\ \hline
[45] & Smarthome & Arduino & MQTT & Grafana–Raspberry Pi \\ \hline
[46] & General application & Computer (simulated sensors) & Modbus & EclipseSCADA–Computer server \\ \hline
[47] & Water pumping & Arduino & Firmata & Node-RED–Raspberry Pi \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Literature survey of IoT-based SCADA systems. 3. We propose a novel method to measure leaf temperature using a low-cost thermal camera, AMG8833 [49]. Our proposed system employs the 8 \(\times\) 8 thermal camera and adopts a simple image thresholding technique to separate the leaf from the background for calculating the leaf temperature. 4. We propose a novel method to visualize the thermal image from the thermal camera in the SCADA HMI, where the data are sent from the sensor using the Modbus TCP protocol via wireless communication and then displayed in the HMI using a graphic tool provided by a free Haiwell Cloud SCADA software. 5. We comprehensively analyze the sensor data concerning vegetation behavior and instrument error reading. The rest of the paper is organized as follows. Section 2 describes our proposed system. It explains the configuration of the proposed IoT-based SCADA, the sensors and leaf temperature measurement method, and the developed HMI. Section 3 presents the experimental results and discussion. Finally, the conclusion is covered in Section 4.
# Metabolic Profiling of Type 2 Diabetes Patients after Bariatric Surgery by Raman Spectroscopy
## 3 Results  
### Clinical Characteristics of Study Groups  
Fourteen subjects with obesity and T2 DM undergoing BS with DDSG-II were included in the study. All patients were examined before and after surgery. At baseline, body weight, BMI, waist and hips circumference, insulin levels, glycemia, HAb1c%, total cholesterol, triglycerides were significantly higher in the Ob-T2 DM group, whereas HDL cholesterol concentrations were lower (Table 1).  
As expected, after a variable post-surgical period of 6-12 months, Ob-T2D patients experienced a significant reduction in all anthropometric measurements (weight, BMI, waist and hips circumference), suggesting a return to non-obese condition. Furthermore, insulinemia improved as evidenced by the decrease in insulin concentrations. There was also a significant decrease in the HbA1c mean values as a result of the T2 DM remission. We observed an expected significant reduction in circulating triglycerides and total cholesterol, as well as an increase in HDL-cholesterol concentrations after BS (Table 1). All these data evidenced that BS is effective in achieving the clinical remission of obesity and T2 DM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Ob-T2 DM** & **BS** \\ \hline Patients & 14 (5 M; 9 F) & 14 (5 M; 9 F) \\ Age, years & 48.5 \(\pm\) 12.4 & 48.5 \(\pm\) 12.4 \\ Weight, kg & 112.4 \(\pm\) 21.7 & 83.9 \(\pm\) 13.9 *** \\ BMI, kg/m\({}^{2}\) & 41.4 \(\pm\) 6.2 & 30.2 \(\pm\) 5.3 *** \\ Waist circumference, cm & 125.2 \(\pm\) 15.1 & 103.9 \(\pm\) 15 ** \\ Hips circumference, cm & 128.4 \(\pm\) 15.9 & 104.6 \(\pm\) 13.4 *** \\ Insulin, mU/mL & 14.1 \(\pm\) 8 & 4.9 \(\pm\) 5.8 *** \\ Glycemia, mg/dL & 158.5 \(\pm\) 41.7 & 82.2 \(\pm\) 10 *** \\ Glycemia 120\({}^{\prime}\), mg/dL & 223.9 \(\pm\) 62.5 & 137.8 \(\pm\) 41.2 *** \\ HAb1c \% & 7.7 \(\pm\) 1.2 & 5.3 \(\pm\) 0.4 *** \\ Total cholesterol, mg/dL & 211.8 \(\pm\) 20.3 & 142.8 \(\pm\) 26 *** \\ HDL-C, mg/dL & 42.6 \(\pm\)7.2 & 52.3 \(\pm\) 4.8 *** \\ Triglycerides, mg/dL & 178.1 \(\pm\) 58.6 & 117.1 \(\pm\) 31 ** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Obese type 2 diabetes mellitus patients [Ob-T2D; \(n=14\)] vs. patients after bariatric surgery [BS]. Data are presented as mean \(\pm\) SD. Shows statistical significance, *** \(p<0.005\); *** \(p<0.0005\).
## 4 Bi-Objective Optimization Allocation Problem Model and Algorithm
### Improved NSGA2 Based on Transcription Gene and Heuristic Recombination (INSGA2-TGHR)  
For how to solve multi-objective optimization problems, there are usually two ideas, one is to use mathematical planning methods to find the exact solution, and the other is to use intelligent computational methods to find the approximate solution. Since this problem is an extension of GQAP and has been proven to be an NP-hard problem, it will become unsolvable when the task and resource size increases, and also commonly used mathematical planning solvers such as gurobi [26] only support mixed integer linear programming and cannot solve quadratic planning problems. On the other hand, intelligent computing is commonly used in non-dominated sorting genetic algorithms (NSGA-II) [12], strength Pareto evolutionary algorithm II (SPEA2) [27], Pareto archival evolutionary strategy (PAES) [28] and multi-objective particle swarm optimization (MOPSO) [29], among which NSGA-II and its improvements performs better in finding a diverse set of solutions and in converging to near the true Pareto-optimal set compared with others [16]. For example, [PERSON] et al. [17] used the benchmark NSGA-II algorithm to solve the time and energy bi-objective optimization problem in mobile cloud computing, [PERSON] [18] took the application criticality and security as optimization objectives and modeled them as a bi-backpack problem using benchmark NSGA-II to solve, and [PERSON] [16] proposed Augmented NSGA-II to achieve good results in a multi-objective optimization problem for smart factory workflow resource allocation.  
Therefore, for the IRMWAP problem, this work proposes the INSGA2-TGHR algorithm. This algorithm is a heuristic genetic algorithm that is redesigned based on NSGA-II,combining the previous ideas for solving GQAP and the workflow assignment problem. Figure 3 shows that when the monitor generates a RMW that needs to be offloaded, it will obtain the CEN resources information to run the INSGA2-TGHR and allocate computing resources to the tasks. As shown in Figure 4, Transcription Gene is used to create the initial population and Heuristic Recombination is used as a crossover operator to generate new offspring. The idea and detail of proposed algorithm will be discussed as follows: