esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 INS/GNSS Concepts
### Standard Tuning of INS/GNSS EKF  
In this subsection, the discussion is based on the work presented by [PERSON] [1]. The primary sources of system noise in the INS are the random walk of velocity error caused by noise in the accelerometer's specific-force measurements and the random walk of attitude error due to noise in the gyroscope's angular-rate measurements. Additionally, if separate dynamic bias states for the accelerometer and gyroscope are not estimated, the in-run variation in the accelerometer and gyroscope biases can be approximated as white noise. For small propagation intervals (\(\leq\)0.2 s), the system noise covariance matrix can be approximated using Equation (30) for the DEKF and Equation (31) for the IEKF, where \(S_{rg}\) and \(S_{ra}\) are the variances of the gyroscope random noise and accelerometer random noise, respectively.  
\[\mathbf{Q}_{INS_{iddf}}=\begin{bmatrix}\mathbf{0}_{3\times 3}&\mathbf{0}_{3 \times 3}&\mathbf{0}_{3\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{S}_{rat}\mathbf{I}_{3}&\mathbf{0}_{3\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{0}_{3\times 3}&\mathbf{S}_{rg}\mathbf{I}_{3}\end{bmatrix} \tag{30}\]  
\[\mathbf{Q}_{INS_{iddf}}=\begin{bmatrix}\mathbf{S}_{rat}\mathbf{I}_{3}&\mathbf{ 0}_{3\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{S}_{rg}\mathbf{I}_{3}\end{bmatrix} \tag{31}\]  
The power spectral densities (PSDs) of accelerometers and gyroscopes at frequencies below 1 Hz are generally white, meaning the standard deviation of the average specific force and angular rate noise is inversely proportional to the square root of the averaging time. As a result, inertial sensor noise is typically characterized by the root of PSD. The common units used are \(\upmu\)g\(/\sqrt{\mathrm{Hz}}\) for accelerometer noise, where \(1\upmu\)g\(/\sqrt{\mathrm{Hz}}=9.80665\times 10^{-6}\) m s\({}^{-1.5}\), and \({}^{\circ}\)/\(\sqrt{\mathrm{hr}}\) or \({}^{\circ}\)/\(\mathrm{hr}\)/\(\sqrt{\mathrm{Hz}}\) for gyroscope noise, with \(1^{\circ}/\mathrm{hr}=2.909\times 10^{-4}\) rad s\({}^{-0.5}\) and \(1^{\circ}\)/\(\sqrt{\mathrm{Hz}}=4.848\times 10^{-6}\) rad s\({}^{-0.5}\). The standard deviations of these random noise samples are obtained by multiplying the corresponding root PSDs by the square root of the sampling rate, or by dividing by the square root of the sampling interval. Since the variance is the square of the standard deviation, this process also allows for the determination of the noise variance over the sampling period. It is important to note that white random noise cannot be calibrated or compensated for, as it lacks correlation between past and future values.  
The tuning of the matrix \(\mathbf{R}\) will not be discussed in this work, as its values will be derived from the GNSS receiver data. Consequently, the novel approach proposed for tuning the INS/GNSS integration will focus on the matrix \(\mathbf{Q}\) and will be discussed in the next section.
## 2 Relative Motion Dynamics Model  
### Port-Hamiltonian System  
A port-Hamiltonian system is composed of a Dirac structure, which illustrates the interconnections of the system's energy components, a resistive structure, which embodies the damping effects, and a Hamiltonian function, which dictates the dynamic behaviors of these energy components [24]. The configuration of input-state-output for port-Hamiltonian systems is specified as follows.  
\[\dot{\mathbf{x}}=(\mathbf{J}(\mathbf{x})-\mathbf{R}(\mathbf{x}))\
abla H(\mathbf{x})+\mathbf{g}(\mathbf{x})\bm {u},\mathbf{x}\in\mathbb{R}^{n},\mathbf{u}\in\mathbb{R}^{m} \tag{1}\]  
\[\mathbf{y}=\mathbf{g}^{\mathsf{T}}(\mathbf{x})\
abla H(\mathbf{x}),\mathbf{y}\in\mathbb{R}^{m} \tag{2}\]  
where \(\mathbf{x},\mathbf{y},\mathbf{u}\) and \(\mathbf{g}(\mathbf{x})\) are the state, output, control signal, and input matrix of the system, respectively. \(\mathbf{J}(\mathbf{x})\) is a skew-symmetric matrix (i.e., \(\mathbf{J}(\mathbf{x})=-\mathbf{J}^{\mathsf{T}}(\mathbf{x})\)), and \(\mathbf{R}(\mathbf{x})\) is a semi-positive definite matrix for \(\mathbf{x}\). In Equation (1), the matrices \(\mathbf{J}(\mathbf{x})\) and \(\mathbf{R}(\mathbf{x})\) are identified as the interconnection matrix and the damping matrix, respectively. The functions denoted by \(H(\mathbf{x})\) are Hamiltonian functions, and \(\
abla H(\mathbf{x})\) represents their partial derivative vectors.  
Remark: By differentiating the Hamiltonian function \(H(\mathbf{x})\), we can obtain  
\[\dot{H}=-\
abla_{\mathbf{x}}^{\mathsf{T}}H(\mathbf{x})\mathbf{R}(\mathbf{x})\
abla H(\mathbf{x})+ \mathbf{u}^{\mathsf{T}}\mathbf{y}\leqslant\mathbf{u}^{\mathsf{T}}\mathbf{y} \tag{3}\]
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 2 Related Work  
Routing protocols can be classified in two broad categories, namely, proactive and reactive. In proactive protocols, the forwarding nodes already know the next node to which the packet has to be forwarded, which reduces the forwarding time. Proactive protocols are, thus, more suitable for delay sensitive data. However, they have some disadvantages as well. They need to update the routing tables whenever the network topology changes or when node failures occur. Scalability is another problem of proactive protocols [11].  
In reactive protocols, a node finds the next hop when it receives a packet to be forwarded [12; 13; 14; 15]. This causes additional delay and makes this type of protocol unsuitable for delay sensitive data. However, periodic updates are not required, saving this overhead, and the network is scalable too.  
Routing protocols can also be classified as location-based or location free. Location-based routing protocols choose the forwarding node on the basis of the location. For example, the next node can be chosen based on its distance to the destination or its depth. The additional measurement device increases energy consumption.  
In the rest of this section, we present an overview of existing routing protocols. Although a taxonomic classification is hard to come by, we have classified the protocols, for the sake of presentation, in these groups: location free, location based, opportunistic, cluster based, energy efficient, mobility based and reliable data deliver protocols.
## 1 Introduction  
With the rapid development of information techniques and the accelerated evolution of industries, the cooperation and interoperation of remote systems are creating more complex systems, which are called systems of systems (SoSs) [1, 2]. Due to their complexity, traditional system testing techniques are only efficient for constituent systems (CSs) but are no longer sufficient to verify SoSs. Simulation-based verification is heavily used for verifying complex systems and is widely employed for analyzing SoS models [3, 4]. A set of input traces has to be selected to exercise the executable model of the SoS properly. The objective of test generation is to identify some input trajectories that can be used for simulation-based verification, assisting in identifying corner cases that expose model errors, thereby increasing the designer's confidence in the model analysis. Several techniques for automatically generating test cases have been defined in the literature, starting from the implementation of a system or model by representing its requirements [5, 6, 7]. Related methods and techniques for SoS verification are still in the preliminary research state,mainly for requirements verification and system verification. There is a lack of efforts in design verification for SoSs; currently, the test cases for design verification are mainly designed manually by SoS designers or architects. Aiming to provide the ability to detect hazardous states at the design phase, we worked on a novel method of automatically generating test cases for model verification in SoSs.  
In this paper, we focus on model-based test generation, which uses a model-based representation to compose the requirements or functions of a system and SoS and automatically generate test cases for design verification. More precisely, we design an approach and a specific algorithm to extend the application of model-based fault-injection test-case generation to the case of an SoS. Fault-based test generation [8] aims to produce a set of test cases that exercise the system under test to stress its fault tolerance. In the context of an SoS, we aim to apply fault-based test generation to identify possible sequences of behaviors that expose hazards at the SoS level. The obtained test cases represent the input for effectively testing the SoS behavior. The main objectives of the paper are to (1) propose an automatic test generation (ATG) approach adopting fault injection and model-based system engineering (MBSE); (2) design an ATG algorithm to support test cases generation; and (3) apply the proposed ATG method in an experimental SoS. The proposed ATG method provides the ability to detect hazardous states at the design phase, assisting design verification for SoS engineering.
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 2 Model
### Validation of the Solar Irradiation Model around the Car (Intensity)  
The above model was compared with the one-year observation result of the solar irradiance to the vehicle, including seasonal (monthly) fluctuation. After fitting to the measurement irradiance dataset, the fitted average shading height is 18.7\({}^{\circ}\) in the direction along the road (local axis) and 12.3\({}^{\circ}\) in the direction orthogonal to the road (local axis), and 15.5\({}^{\circ}\) as the average. The averaged shading height after data fit was 15.5\({}^{\circ}\), and it is very close from the value of rough physical measurement (15\({}^{\circ}\) on average). The average reflectance from the road is also fit to the measured data and obtained as 0.088, also a reasonable value for aged asphalt (typical value is 0.07). The comparison to the measured irradiance and modeled irradiance are compared in Table 2.  
The validation is also done by a one-month integration (seasonal fluctuation). The model met the modeled values in every month (Figure 15). The trend of the residual errors could be explained by the difference in climate from the regular year (for example, cloudy in summer).  
The same comparison is made on a daily base (Figure 16). Since every day's climate is not always identical to the one in the solar database, there are significant errors in the time-series trend. However, the range of the distribution in the modeled irradiance is almost the same as the measured irradiance.  
Figure 14: Monitored the result of the solar irradiance on the car roof and car sides in the driving route in Figure 13.  
Figure 15: Monthly-based comparison between the measured solar irradiance around the car (bar-chart) and modeled (typical year from the METPV-11 solar database, namely, MEteorological Test data for PhotoVoltaic system) solar irradiance on the car (line-chart): (**a**) car roof irradiance; (**b**) car side irradiance.
## 4 Numerical Examples
### Estimation Performance  
In this experiment, we discuss the estimation accuracy of the designed waveform. In order to achieve the root mean square errors (RMSEs) of range and velocity estimations, 500 independent Monte Carlo experiments are performed per SCNR. The RMSEs of the range can be defined as follows:  
\[R_{rmse}=\sqrt{\frac{1}{N_{e}}\sum_{N_{e}}|\hat{r_{t}}-r_{t}|^{2}}, \tag{26}\]  
where \(N_{e}\) is the number of Monte Carlo experiments, \(\hat{r_{t}}\) is the estimated value of the range, and \(r_{t}\) is the actual value of the range. Similarly, we can obtain the RMSEs of the velocity by replacing the estimated and actual values of the velocity into (26). In each Monte Carlo experiment, the velocity of the target is generated randomly. The other parameters are the same as the detection performance experiment. The results are shown in Figure 12. The plots show that the RMSEs of velocity and range estimations decrease with an increase in the SCNR. Moreover, the designed waveform and the uniform waveform applying IAA have very close velocity and range estimation accuracies.  
In Figure 12a, we notice that the RMSEs of the velocity of the designed waveform using the same algorithm are slightly worse than the RMSEs of the velocity of the uniform waveform when SCNR \(<-22\) dB. The reason for this is that the ISLR of the velocity ambiguity function of the uniform waveform is smaller, and the accuracy is higher when FTPR is adopted. When SCNR \(>-22\) dB, the RMSEs of the velocity of the designed waveform and the uniform waveform are similar when applying the same algorithm. In addition, the RMSEs of the velocity of the IAA method are better than that of the OMP method, which are better than that of the DFT or NUDFT methods. The reason for this is that the IAA and OMP methods are able to suppress the sidelobe.  
Figure 11: SCNRout vs. velocity for different waveforms and algorithms.  
In Figure 12b, we notice that the RMSEs of range decrease as the SCNR increases. The RMSEs of range for the designed waveform and the uniform waveform applying the IAA are very similar to the results of the RMSEs of velocity. However, the difference is that the RMSEs of the range of the designed waveform applying the OMP and NUDFT are slightly worse than those of the uniform waveform applying the OMP and DFT. This is due to the reduction in the number of sensing symbols, which reduces the performance of the range estimation, applying the OMP and NUDFT methods.
# Numerical Investigation on the Principle of Energy Separation in the Vortex Tube
## 3 Results and Discussion
### Temperature Separation
[28] developed a prediction model for the hot and cold exit temperatures, which is based on the heat pump theory and requires only the inlet and exit pressures. However, since turbulent eddies may also contribute to temperature homogenization, that model is not complete. In this work, the hot exit temperature increase was attributed to the large axial pressure gradient. The higher the cold mass fraction, the larger the axial pressure gradient was. This is because the pressure in the region near the hot exit must be sufficiently high enough to create a reverse flow in the core toward the cold exit, as the hot exit area is reduced by adjusting the hot end valve. A swirling vortex flow results in decreases in cold exit temperature and core-region pressure, and the cold exit temperature depends on the cold mass fraction. As shown in Figure 5, the lower flow rate leaves the cold exit, and the larger negative pressure zone appears near the cold exit. If the negative pressure zone is too large, a partial backflow of atmospheric air at 294 K through the cold exit may occur. In that condition, the decrease in cold exit temperature decreases as the cold mass fraction decreases. This phenomenon was also reported by [47]. It is also seen in Figures 3b and 7 that the decrease in the cold exit temperature for cmf = 0.8 is smaller than that for cmf = 0.5. If the cold mass fraction is large enough that a partial backflow does not occur through the cold exit, then the cold exit temperature decreases as the cold mass fraction increases. The first reason is that the overall temperature inside the tube increases as the cold mass fraction increases. The second reason is that the core vortex flow moving toward the cold exit serves to transport the hot air in the hot exit region to the cold exit region. Therefore, as shown in Figure 7, for a high cold mass fraction, the temperature in the near-axis region tends to be higher than that in the periphery region. It is interesting to notice that the temperature does not always increase in the radial direction.
## 5 Results and Discussion
### Machine Learning Results  
Interference factors during image formation often make it difficult to achieve accurate classification of targets. Ensuring adequate training and testing data, besides selecting a suitable classifier, are the main pillars for delivering veracious classification. Toward that aim, the outputs of all the previously mentioned image-processing techniques, the spectral signatures of the different rock units, previous mapping, and field observations have been used as inputs for lithological classification utilizing the support vector machine algorithm. Consequently, for a more accurate selection of training sites in this study, three files were used: (1) all lithological data from compiled previous geological maps; (2) several effective RGB color composites resulting from various image-processing techniques, such as OIF, BR, PCA, and MNF; (3) the locations of rock samples.  
The sample types in the study area are divided into seven categories: mantle peridotite, glutenite, slate, Gondwana granite, quaternary, water body, and hillshade. Accordingly, training areas for seven classes were determined and fed into the SVM algorithm. A total of 11,423 pixels representing seven targets were selected. The separability of each category exceeds 1.9. Secondly, by creating training samples from the region of interest (ROI) and using the support vector machine classification (SVM) method (algorithm) to classify the entire image, each pixel is merged into the known sample category created in advance. After classification, the Majority tool is used to remove small patches in the classified image  
Figure 10: Mantle peridotite discrimination using OLI false color composites of (**a**) PC4-PC4-IC2; (**b**) ICA2-MNF5-PCA4 in RGB.  
Figure 9: Lithological discrimination using OLI minimum noise fraction of (**a**) MNF3-MNF5-MNF2; (**b**) B7-MNF5-MNF3 in RGB.  
(Figure 11). Finally, these results are evaluated and compared using the overall accuracy and Kappa coefficient. The Kappa coefficient is distinct from the overall accuracy since it considers the complete contingency matrix and is intended to evaluate the consistency of the results [69,86]. The classification accuracy is evaluated using a confusion matrix, and the results shows overall classification accuracy of 94.98% and a Kappa coefficient of 0.93. However, misclassification still occurs in some areas, which is due to the mixed pixels caused by spectral resolution. Hence, field verification is required.
## 3 Case Study: Lishanyuan Landslide
### Comparative Analysis  
Using the same thresholds, the traditional differential method is also applied to calculate the early warning based on the monitoring data of the two GNSS monitoring stations. As shown in Figures 9 and 10, the calculated velocity, acceleration, and normalized tangent angle have strong fluctuations, which makes it difficult to reflect the deformation state of the landslide.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Event Time** & \begin{tabular}{c} **Cumulative** \\ **Deformation (mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Velocity zhanginyu** \\ **(mm/d)** \\ \end{tabular} & **Acceleration (mm/d2)** &
\begin{tabular}{c} **Normalized Tangent** \\ **Angle (°)** \\ \end{tabular} & **Warning Event** \\ \hline
25 April 21:00 & 21.11 & 4.34 & 0.85 & 68.18 & Exceed \(S_{u}=21\) mm \\
28 April 05:00 & 42.29 & 10.16 & 3.65 & 80.30 & Exceed \(S_{u}=42\) mm \\
28 April 18:00 & 46.75 & 10.84 & \(-\)0.69 & 80.89 & Blue warning cancelled \\
17 May 08:00 & 93.65 & 1.77 & 3.62 & 45.47 & Blue warning \\
17 May 17:30 & 99.12 & 3.36 & 6.7 & 62.64 & Yellow warning \\
18 May 00:30 & 114.63 & 10.15 & 24.63 & 80.29 & Orange warning \\  & & & & & Red warning, \\
18 May 03:30 & 123.01 & 14.08 & 32.85 & 82.97 & Alternative red and \\  & & & & & orange warnings \\
26 May 11:30 & 392.11 & 9.78 & \(-\)7.34 & 79.93 & Yellow warning \\
28 May 12:30 & 400.91 & 2.92 & \(-\)4.49 & 59.24 & Blue warning \\
29 May 09:00 & 401.6 & 1.61 & \(-\)2.1 & 42.90 & Safety \\ \hline \hline \end{tabular}
\end{table}
Table 4: Key early warning events at the LISHAN-DB02 monitoring station.  
Figure 7: Early warning results of the LISHAN-DB02 monitoring station obtained by the proposed method. (**a**) Results of the conventional warning method based on velocity and acceleration. (**b**) Results of the critical sliding warning method based on the normalized tangent angle.  
Figure 8: Photos of the Lishanyuan landslide on 19 May.  
Figure 9: Early warning results of the LISHAN-DB01 monitoring station obtained by the traditional difference method. **(a)** Conventional warning based on velocity and acceleration **(b)** critical sliding warning based on the normalized tangent angle.  
There were intensive warnings during the accelerating deformation stage on 17 May, but they were mixed with a large number of false warnings, so the percentage of real warnings is relatively low. The curves of velocity, acceleration, and normalized tangent angle calculated by the proposed method are smooth, and can reflect each acceleration deformation process, thereby reducing false warnings.
## 2 Vehicle Model Used in Agricultural Vehicle Path Tracking Control
### Dynamic Vehicle Model  
The agricultural vehicle dynamic model is usually based on [PERSON]'s second law for navigation controller design. The control system has good robustness to changes in external parameters because of considering the force during the movement of agricultural machinery. To improve automatic control performance under high speeds, [PERSON] et al. [34] proposed a yaw dynamics model, which was proved to be an improvement on the tractor controller based on the kinematic model. [PERSON] et al. [35] considered the influence of vehicle steering dynamics and developed a path-tracking controller using the backstepping algorithm. Compared with the kinematic model, the algorithm based on the influence of dynamic factors improves the controller performance. Based on the tractor two-wheel dynamics model, [PERSON] et al. [36] proposed an improved Stanley controller (IMP-ST), and optimized controller parameters through multiple population genetic algorithm (MPGA) to achieve better tracking performance. In the research of a path-tracking control algorithm for caterpillar robots, [PERSON] et al. [37] established the dynamic model of the interaction between the caterpillar chassis and the soil by using multi-body dynamic simulation software. As shown in Figure 5, the simplified dynamic model of the caterpillar robot is presented. In Figure 5, \(O\) is the instantaneous center of rotation, \(O^{\prime}\) is the geometric center, and \(R\) is the turning radius of the robot, mm.