esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Security Analysis
### Statistical Random Number Tests
#### 4.3.1 Random Test Results under FIPS PUB 140-1  
First, we put the output keystream of the proposed improved CML ciphers under FIPS PUB 140-1 for the random test. We select different random keys and different random initial values to produce 100 output keystreams. Each keystream length is 20,000 bits. Table 3 shows the test results of FIPS PUB 140-1 of the improved CML based on the logistic map, tent map, and the improved compound chaotic CMLs, respectively. From this table, we can find that the pass rate of each test is 100%. It shows they have good statistical properties under the test of FIPS PUB 140-1.
## 4 Discussion
### Impact of MODIS LAI Errors on FAPAR Estimation  
Although the accuracy of FAPAR\({}_{\rm FVC}\) has been clearly better than that of FAPAR\({}_{\rm LAI}\), it still has clear errors and the errors are still dominated by overestimation (Figure 3), which suggests that there are other factors affecting the accuracy of FAPAR in addition to the FVC. Since FAPAR\({}_{\rm MOD}\), FAPAR\({}_{\rm LAI}\) and FAPAR\({}_{\rm FVC}\) are all calculated based on the MODIS LAI products based on the Beer-Lambert's law, the estimated FAPAR error may be related to the LAI error. The observation in this study indicated there was a significant correlation between LAI error (difference between MODIS LAI and in situ measured LAI) and FAPAR error (difference between estimated FAPAR and in situ measured FAPAR).  
From all samples, the samples at biome types with FAPAR\({}_{\rm MOD}\) overestimated and the samples at biome types with FAPAR\({}_{\rm MOD}\) underestimated, LAI error was all significantly positively associated with errors from FAPAR\({}_{\rm MOD}\) (Figure 8). Therefore, in addition to the factor of FVC affecting the estimated FAPAR accuracy, the accuracy of the MODIS LAI products used also have an important effect. For the three groups of samples, after considering the FVC, the ability of LAI error to explain variation in FAPAR error was reduced by basically more than 50% (Figure 8). Considering that there may be other factors which affect FAPAR accuracy, the reduction in FAPAR estimation error by considering the FVC may be roughly comparable or higher than the LAI-induced error (Figure 8).  
The overestimation and underestimation of FAPAR\({}_{\text{FVC}}\) are primarily associated with errors in MODIS LAI. As shown in Figure 8, the underestimation samples are derived from tropical broadleaf evergreen forests and boreal forests, where MODIS LAI is consistently underestimated across all samples. This underestimation of LAI consequently led to the underestimation of FAPAR in these sites. Conversely, at other sites where overestimation occurs, the number of samples with overestimated LAI clearly exceeds those with underestimated LAI, resulting in an overestimation of FAPAR.
# Development of a Hardware-in-the-Loop Platform for a Teleoperation Flexibility Robotic System
###### Abstract  
A control method for a cable-driven robot in a teleoperation system is proposed using the hardware-in-the-loop (HIL) simulation technique. The main components of the teleoperated robotic system are a haptic device, also called a delta robot, and a cable-driven hyper-redundant (CDHR) robot. The CDHR manipulator has higher flexibility and multiple degrees of freedom (DOF), and, therefore, its inverse kinematics are complex. For this reason, the Jacobian method is used in place of the conventional method to calculate the inverse kinematics. Moreover, the two robots constituting the telerobotic system are different in terms of their mechanical structures and workspaces. Therefore, the position mapping method is applied to ensure that the two workspaces are utilized together. However, a singularity area appears when the mapping parameter is adjusted to expand the workspace. Therefore, a haptic algorithm is proposed to prevent the robot from moving into the singularity region and generate force feedback at the end-effector of the haptic device to warn the operator. Because experimental verification of this control strategy is difficult, the HIL technique is used for demonstration in this study to ensure stability and safety before implementation of the method at the experiment scale. The CDHR robot is designed using SolidWorks 2021. Then, the Simscape model is used to simulate the telerobotic system. In addition, the protocol between the haptic device and the laptop is programmed using C/C++ language to facilitate communication with the CDHR robot in MATLAB Simulink 2022a. A few trials are conducted to evaluate and demonstrate the effectiveness of the proposed method.  
teleoperation system; hardware-in-the-loop simulation; redundant manipulator; Novint Falcon
## 2 Materials and Methods
### Stability Area  
The load's characteristics (dimension, weight, etc.) and the route determine the number of modules used and their hydraulic configuration. After settling the number of axles and the type of trailer, it is necessary to decide how to group the cylinders for a haulage arrangement that is as stable as possible. Each of the trailer's modules has a hydraulic system. It consists of various valves, pipes, and cylinders that enable several trailers to connect hydraulically. In practice, the grouping of cylinders is accomplished by a set of valves below the trailer's platform. Figure 3 shows a configuration of valves that create three groups that are known as 3-point suspension. The minimum number of groups is three, which is actually the most stable configuration.  
Figure 1: Modular trailer axle compensation [28].  
Figure 2: Hydraulic suspension system.  
The transport is deemed to be stable if the total force projection is within the stability area. If this projection is not within the stability area, there is a risk of damage to the trailer and/or of tipping. If the force projection is vertical, inertial forces do not affect the stability of the trailer (deceleration or acceleration, turning, and/or wind force). The corners of the area of stability define a plane that can be defined as \(\text{ar}+\text{b}y+\text{c}z-\text{K}\_0=0\) (plane \(\Pi\)). This can be calculated by solving the following determinant:  
\[\left|\begin{array}{ccc}x-x_{0}&y-y_{0}&z-z_{0}\\ x_{1}-x_{0}&y_{1}-y_{0}&z_{1}-z_{0}\\ x_{2}-x_{0}&y_{2}-y_{0}&z_{2}-z_{0}\end{array}\right|=0 \tag{3}\]  
with \((x_{0},y_{0},z_{0})\), \((x_{1},y_{1},z_{1})\), and \((x_{2},y_{2},z_{2})\) as the coordinates of the centers of the groups 0, 1, and 2, respectively. One can calculate these coordinates' geometrically by using the location of the junction of the axles and cylinders or analytically is as follows:  
\[\begin{array}{l}x_{i}=\frac{1}{n_{i}}\cdot\sum\limits_{j=1}^{n_{i}}X_{ij}\\ y_{i}=\frac{1}{n_{i}}\cdot\sum\limits_{j=1}^{n_{i}}Y_{ij}\\ z_{i}=\frac{1}{n_{i}}\cdot\sum\limits_{j=1}^{n_{i}}Z_{ij}\end{array} \tag{4}\]  
with \((xi,yi,\text{ and }zi)\) as coordinates of group \(i\), \(n_{i}\) as the number of group i pendulum axles, and \((X_{ij},Y_{ij},\text{ and }Z_{ij})\) are the pendulum axle fulcrum \(j\)'s coordinates in group \(i\). Camber and inclination of the road change the spatial position of the cylinders and, therefore, the coordinates of the groups. These inclinations can increase or decrease the safety, depending on the stability area's definition and the forces that are acting on the transport.
## 3 Results and Discussion
### Gold Electrodes Biofunctionalization and Characterization  
The schematic diagram of the biological functionalization step of immobilizing the antibody on the flexible Au electrode is shown in Figure 3. First, by incubating 11-MUA, a stable self-assembled monolayer (SAM) was formed on the surface of the gold electrode. Since 11-MUA has a long alkyl chain, it can provide van der Waals force between the molecules, forming a solid, close-packed two-dimensional (2D) molecular array [24, 25]. As part of the conventional standard process for modifying the SAM layer on gold surface, this method has been adopted by most researchers for its convenience and practicality. The concentration of 11-MUA used in this study has been confirmed in most literatures, and is capable of immunoassay [18, 26, 27]. Then the NHS/EDC is added to activate the SAM layer before the amino group of the protein molecules were stably bounded to the activated SAM layer. Finally, ethanolamine molecules are used to block the sites of unbounded protein molecules to avoid non-specific binding during antigen detection.  
In order to confirm the formation of the self-assembled monolayer (SAM) on the surface of the gold electrode, we conducted Atomic Force Microscope (AFM) tests on the gold electrode surface before and after biofunctionalization (shown in Supplementary Figure S2).  
Figure 2: (**a**,**b**) show the impedance measurements and fitting values of PBS solutions of five different concentrations within the linear interval; (**c**) the relationship between the equivalent resistance Rs and the concentration; (**d**) compares the relationship between PBS concentration and equivalent resistance with previous work.
# Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
## 2 RBF-BP Combined Neural Network Model
### CPSO-RBF-BP Reactor Temperature Model Construction  
The BP neural network has the issue of easily settling on a local optimal solution, which prevents it from achieving a temperature prediction effect. The CPSO-RBF-BP neural network model is created utilizing the complementing benefits of the RBF-BP combinatorial neural network and the chaotic particle swarm algorithm as a remedy to the drawbacks of the BP neural network. The precise procedures for developing the RBF-BP mixed neural network model using CPSO optimization are outlined in this study. The flow chart of steps is shown in Figure 5.  
Step 1: Determine the structure of the RBF-BP combined neural network, choose five important factors affecting reactor temperature as input eigenvalues, choose temperature as the output, set the number of nodes in the input layer to 5, the number of nodes in the hidden layer to 8, and the number of output nodes to 1.  
Step 2: The temperature data of Hengdian Chemical Enterprise's reaction kettle in normal production was gathered and processed consistently, and then distributed [0, 1].  
Step 3: Begin the chaotic particle swarm by determining the population size N, the maximum number of chaotic iterations M, the inertia weight, the learning factors C1 and C2, and other essential parameters.  
Step 4: To determine the global optimal fitness and optimal position, the fitness of each PSO individual was calculated and sorted.  
Step 5: The output is replaced by the new solution after the global optimal solution has been chaotically iterated.  
Step 6: Determine whether or not the termination condition has been met. If the termination condition is met, the weight and threshold value are output; otherwise, step 4 is repeated.  
Step 7: Set the output weight and threshold for the RBF-BP neural network.  
Step 8: Samples were used to train the RBF-BP neural network, and the prediction accuracy was checked.  
Figure 5: Specific model creation stages for the CPSO-RBF-BP.
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 6 Conclusions  
Owing to its capability of precisely detecting small amplitude vibration motions, Doppler radar technology has been used for noncontact vital sign detection. In the past few decades, especially in the new century, Doppler radar has attracted significant attention from researchers across the world and numerous important advancements have been made to push the technology further into practical applications. Various hardware architectures have been proposed to tackle different scenarios and issues associated with biomedical applications. Although some radar systems are still in the early prototype stage, they have proven the possibility and laid the foundation for the following development to make the technology ubiquitous in life applications. The random body movement was studied and several approaches such as the camera-radar hybrid system were proposed to solve the issue. The digital post-distortion technique was proposed to solve the signal distortion problem that happens in AC coupled radar, without need to modify the hardware architecture. To overcome the Doppler radar incapability of ranging, a hybrid FMCW-interferometry radar was designed to be able to measure distance and displacement at the same time. While radar hardware is important, technical advancements have also happened on the signal processing side. For example, the techniques to mitigate \(I/Q\) mismatch make the radar sensor more robust in displacement sensing. The radar technique is also promising in numerous emerging applications such as gesture sensing for human-machine interaction and occupancy sensing. It is expected that, with the joint efforts from researchers in the radar community, more advancements will be seen in Doppler radar to push the technology into more practical applications. Some emerging applications such as gesture sensing seem futuristic now but could become reality soon.  
The author conducted the literature review and write the manuscript.  
The author declares no conflict of interest.
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
## 3 Distributed State Estimation Algorithm with False Data Injection Attacks
### Injected Data Detection  
In cyber-physical systems, attack detectors based on the analysis of the statistical properties of the residual are developed to detect FDI attacks [22; 23; 27]. In this paper, we adopt the real-time, residual-based \(\mathcal{O}^{2}\) detector to determine whether FDI attacks exist in the radar network by analyzing the statistical properties of the residual.  
If there are no attacks for radar \(i\), the measurement equation can be expressed as  
\[\mathbf{y}_{i}(k)=\mathbf{H}(k)\mathbf{x}_{i}(k)+\mathbf{v}_{i}(k) \tag{11}\]  
Denote the estimation error as \(\mathbf{e}_{i}(k)=\mathbf{x}(k)-\hat{\mathbf{x}}_{i}(k)\) and the residual of radar \(i\) as \(\Delta\mathbf{y}_{i}(k)=\mathbf{y}_{i}(k)-\mathbf{H}(k)\mathbf{x}_{i}(k)\). The estimation error satisfies \(\mathbf{e}_{i}(k)\sim\mathcal{N}(0,\mathbf{P}_{k-1})\) and the residual \(\Delta\mathbf{y}_{i}(k)\sim\mathcal{N}\left(0,\mathbf{P}_{k|k-1}\right)\), where \(\mathbf{P}_{k|k-1}=\mathbf{H}(k)\mathbf{P}_{k-1}\mathbf{H}^{T}(k)+\mathbf{R}(k)\).  
We adopted the real-time, residual-based detector to determine whether FDI attacks exist, and the detection criterion is given as in [22]:  
\[J_{i}(k)=\Delta\mathbf{y}_{i}^{T}(k)\mathbf{P}_{k|k-1}^{-1}\Delta\mathbf{y}_{i}(k) \lessgtr J_{th} \tag{12}\]  
Note that since \(\Delta\mathbf{y}_{i}(k)\) obeys the Gaussian distribution, \(J_{i}(k)\) obeys \(\mathcal{O}^{2}\) distribution with the degree of freedom \(d\), where \(d\) is the dimension of \(\mathbf{y}_{i}(k)\). The threshold value \(J_{th}\) is an alarm-triggering threshold. If \(J_{i}(k)>J_{th}\), then it is considered as radar \(i\) being under attack, and the detection flag is set to \(\beta_{i}=1\). Otherwise, radar \(i\) is not under attack, and the detection flag is set to \(\beta_{i}=0\). Additionally, note that the SUM detector can be used to detect carefully crafted FDI attacks (see [22]).
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Geographical Areas and Weather Data  
2.1.1. The Island of Sardinia and the Rice Districts  
The SMARTerra infrastructure is specifically developed for Sardinia, which, with an area of over 24,000 km\({}^{2}\), is the second largest island in the Mediterranean Sea. Nevertheless, it can be readily adapted to other geographical regions. The Sardinian landscape ranges from fertile plains to rugged coastlines and mountain areas, resulting in distinct microclimates that may variably influence the development of pests and plant diseases (Figure 2, left).  
This study will show, at 10 m resolution, the alert level for rice blast disease on paddy fields in the rice-growing districts of Oristano and San Gavino and, at 100 m resolution, the prediction for locust egg hatching for the entire region of Sardinia.  
Figure 2: The digital elevation model (DEM) of Sardinia, with the island’s main rice-growing areas (**left**). The DEM was obtained by processing 10 m resolution rasters downloaded from the geoportal of the region of Sardinia (2014). On the (**right**), the locations of the meteorological stations of the Regional Meteorological Network (RUR) are shown in yellow, and the nodes of the grid points for which the BOLAM model provides weather forecasts are shown in white.  
The SMARTerra backbone can be adapted, with minor modifications, to other geographical areas equipped with agrometeorological station networks, provided that local weather models are available.
However, for fault diagnosis problems, the training data volume is much smaller than that of the image processing, which means that the depth model is difficult to obtain ideal results in dealing with fault diagnosis problems independently. For SVM, the idea of kernel function is adopted to map nonlinear samples to a high-dimensional space and make it linearly separable, and then SVM finds an optimal hyperplane that maximizes the classification interval between data sets, thus promoting classification performance [22]. Many research works have proved that the SVM has a strong learning ability for small sample classification problems. Therefore, by combining 1 DCNN with the SVM, the extracted sparse representative feature vectors from the second fully connected layer of the network are taken as the inputs of the SVM for achieving optimal classification accuracy. However, the standard SVM needs to preset two crucial parameters (i.e., penalty factor c and kernel parameter g), which have a great influence on its classification performance [23]. To solve this problem, various optimization methods are applied to tune SVM parameters, such as genetic algorithm (GA) [24] and particle swarm optimization (PSO) [23]. Recently, the particle swarm optimization algorithm has achieved remarkable results in many works. However, the original PSO algorithm has some drawbacks, e.g., premature and slow convergence speed. To avoid those shortcomings, this paper proposed an improved novel particle swarm optimization algorithm (INPSO) for SVM parameter optimization. In this paper, we propose a novel fault diagnosis method based on the one-dimensional convolution neural network (1 DCNN) and SVM optimized by INPSO (INPSO-SVM) to improve diagnosis accuracy. To sum up, there are three main contributions existing in the proposed method. Firstly, the proposed method directly extracts the features of the original time-domain vibration signals by applying a 1 DCNN network, without any time-frequency conversion. Secondly, the excellent small sample learning abilities of the SVM is combined with the powerful deep feature adaptive learning ability of 1 DCNN to improve the diagnosis accuracy. Finally, an improved particle swarm optimizer variant is presented to tune the parameters of SVM, which can improve the classification accuracy and generalization performance of SVM. The engineering application and contrastive tests reveal the effectiveness and superiority of the proposed method. The remainder of this paper is organized as follows: Section 2 is dedicated to the basic knowledge of CNN, SVM, and PSO. Section 3 introduces the proposed fault diagnosis method based on 1 DCNN model and INPSO optimized SVM. Section 4 discusses the results of the experiments. The conclusion is summarized in Section 5.
## 3 Results
### Variation of \(T_{\rm UM}\) vs. \(T_{\rm L}\) over Greece  
There are few studies worldwide that deal with the Unsworth-Monteith turbidity coefficient because of its dependence on the water-vapor content in the atmosphere, as stated in Section 3.1. However, those that exist show a straightforward linear dependence of \(T_{\rm UM}\) on \(T_{\rm L}\) and vice versa. Figure 14 shows this dependence for all and clear-sky conditions over Greece.  
[PERSON] et al. [24] provided the relationship \(T_{\rm UM}=-0.11+0.07\)\(T_{\rm L}\), R\({}^{2}=0.88\) for Athens in the period 1975-1984. Furthermore, [PERSON] et al. [25] provided the expression \(T_{\rm UMvis}=-0.1338+0.1360\)\(T_{\rm Lvis}\), R\({}^{2}=0.99\) for Athens in the period 1992-1995, where _vis_ refers to the visible band of the solar spectrum. In an updated work about the Linke and Unsworth-Montheith turbidity parameters for Athens, [PERSON] et al. [26] provided the relationship \(T_{\rm UM}=-0.1690+0.0939\)\(T_{\rm L}\), R\({}^{2}=0.97\) in the period 1975-1995, while [PERSON] et al. [76] derived the expression \(T_{\rm UM}=-0.182+0.0837\)\(T_{\rm L}\), R\({}^{2}=0.99\) for the same site in the period 1954-1991. It is interesting to note that the coefficients of these equations for Athens are in close agreement with those provided for clear-sky conditions for all climatic zones in Greece (see Figure 14). [PERSON] et al. [72] provided a relationship for \(T_{\rm UM}\) as a function of \(\beta\) for Valencia, Spain in the period 1990-1996 instead of \(T_{\rm L}\).  
Figure 13: Map of annual mean \(T_{\rm UM}\) values over Greece for all (upper graph) and clear-sky (lower graph) conditions during TATY.  
Figure 14: Monthly mean \(T_{\rm UM}\) vs. \(T_{\rm L}\) data pairs for all sites under all (upper graph) and clear-sky (lower graph) conditions in TATY and their best-fit lines. Linear regression equations: for all-skies \(T_{\rm UM}\) = \(-0.2320\) + \(0.1120\)\(T_{\rm L}\), R\({}^{2}\) = 0.91; for clear skies \(T_{\rm UM}\) = \(-0.1900\) + \(0.0980\)\(T_{\rm L}\), R\({}^{2}\) = 0.96.