esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, we propose an SRL-based framework for the longitudinal vehicle dynamics control of a CACC system. A supervisor trained by driving data from a human driver is introduced to guide the reinforcement learning process. During the learning process, the composite action of the supervisor network and the actor network is sent to the system, and the actor network is updated with the error provided by the supervisor and the critic network simultaneously. The gain scheduler adjusts the weighting between supervisor and actor to improve the learning efficiency and obtain a near-optimal control policy. The simulation results show that the performance of the proposed control strategy is superior to that of the linear controller, and the human driver's acceleration characteristics can be successfully mimicked by the SRL-based strategy, resulting in an improving driver comfort and acceptance of the CACC system. In addition, the proposed framework is demonstrated on CACC test vehicles, and the effectiveness is validated by vehicle-following tests with satisfactory performance. Notably, the SRL algorithm can be implemented in a real-time way, such that the driver's driving data are collected for online updating of the actor network to provide a human-like control policy. In the next step, the string stability for vehicle platooning will be considered in the SRL approach to reduce the impact of the trained control policy on traffic flow.  
Methodology, writing, and original draft preparation: S.W.; conceptualization: S.W., Y.Z., X.Z. and W.W.; simulation and experiments: S.W., T.Z., and X.Z.; review & editing: [PERSON] and X.Z.; supervision: [PERSON]; funding acquisition: Y.Z. and W.W.  
This research was funded in part by the National Key R&D Program of China under Grant 2017 YFB0103801 and in part by the National Natural Science Foundation of China under Grant 51775039.  
The authors declare no conflict of interest.  
Figure 12: Test results with a 1.2-s time headway: (**a**) velocity, (**b**) acceleration, and (**c**) distance.
## 3 Theoretical Analysis and Proof
### Experiments with Different Datasets
#### 4.3.4 Hj-2a  
The sea ice image used in this experiment is obtained from the HJ-2A satellite, as shown in Figure 6a, with a size of 288 \(\times\) 246 pixels and a spatial resolution of 16 m. Apart from some fragmented and dense sea ice, there is also fog cover in the left and upper-right parts of the image, posing significant challenges for the four models. As demonstrated in Figure 6 and Table 4, the four models perform equally well in noise-free environments, and their convergence results are impressive. Upon introducing random noise of \(\psi(t)\in 5\times[-1,1]\), a decline in feature identification performance is observed across all four models. EAEND model (10), in particular, fails to identify certain sea ice fragments that share similar coloration with water. This observation aligns with the numerical results and is visually corroborated in Figure 6g. Both MNI model (27) and NRI model (28) exhibit a tendency to misclassify fog and fragmented ice as sea ice, yielding Kappa coefficients of 0.455939 and 0.491205, respectively. Additionally, NRI model (28) demonstrates marginally better performance than MNI model (27) in terms of OA, AA, and PP values. The most notable underperformance is seen in ETZNN model (29), which completely fails to detect any sea ice in this experiment. Its overall performance significantly deteriorates compared to the previous three experiments.
## 2 Materials and Methods
### PCM Enhanced Battery Packs  
In all times, a BTMS in an electric vehicle is required for the proper operation of a traction battery. The primary purpose of this system is to maintain the battery at an optimal average temperature range. It must meet the car manufacturer's specifications. This would be a small and lightweight design that is economical, dependable, easy to maintain, and uses little parasitic power. A BTMS can be passive or active, and it can use air or liquid for cooling and heating [2]. Due to cost, size, and space constraints, as well as their use in warm regions, early car battery packs did not use heating or cooling devices, instead relying on ambient air blowing in to remove heat from the batteries as shown in Figure 1.  
The first prototypes used passive ambient air cooling. Cabin air is used today to cool/heat the packages as shown in Figures 2 and 3. Although the ambient air is heated and cooled by the vehicle's air conditioning or heating system, it is still considered a passive system [2].  
Figure 1: Passive cooling with ambient air [2].  
For these passive systems to function successfully, the ambient air temperature must be between 10 \({}^{\circ}\)C and 35 \({}^{\circ}\)C. Otherwise, the package's performance will decrease in extreme cold or heat. Active components such as evaporators, heater cores, engine coolants, and even electric and fuel-fired heaters are required outside of these limits [2].  
For thermal management of electric vehicles, common heat dissipation techniques including forced air cooling and liquid cooling are frequently employed. Such BTMS first seem to be highly effective, but they often increase the overall system's weight and parasitic power requirements by adding extra blowers, fans, pumps, pipes, and other accessories that are large, expensive, and complex [2]. These cooling system limitations, the increased energy consumption of parasitic loads, and the limited battery capacity can all be avoided by using a well-designed and optimized PCM system [3]. Many advantages can result from a novel thermal management approach that uses PCM as a heat dissipation source. A compact, cost-effective, and warp-resistant system without ancillary performance requirements ought to be the primary benefit of such a heat management system [3].
## 3 Image Cryptography
### Symmetric Encryption  
The symmetric cryptography paradigm defines a single shared key for both encryption and decryption functions. As a result, the process of cryptography is easier to implement. However, the biggest challenge is how to securely distribute the shared key. In fact, it is not a trivial task, because it is not always possible to pre-deploy or pre-distribute keys in sensor nodes [6].  
A brief description of some of the most relevant symmetric encryption algorithms is presented as follows:  
* Advanced Encryption Standard (AES): This is one of the most popular symmetric encryption algorithms [27]. Also known as Rijndael, AES is an encryption scheme by blocking used in large-scale systems. In WSN, this is the main mechanism of encryption adopted by the WirelessHART standard [28]. An energy-efficient security scheme that uses AES as the main encryption algorithm for WSN is presented in [29].
* Data Encryption Standard (DES): This is a low-complexity algorithm that uses a small 56-bit key [30]. Despite some failures, DES was studied more thoroughly in academia, motivating the development of modern systems of cryptanalysis.
* International Data Encryption Algorithm (IDEA): This algorithm is a block cipher designed to be the replacement for DES [31]. It exploits confusion and diffusion to produce the cipher text, with 128-bit keys and the use of XOR gates, 16-bit addition and multiplication (as operations are made with blocks of 16 bits, the algorithm is very efficient in 16-bit microprocessors, common in sensor motes).  
All of these algorithms can be used in WSN, each one with advantages and drawbacks. Additionally, there is another type of encryption algorithm called the hash function, such as MD5 and SHA-1. However, they have higher overhead than the algorithms mentioned before, and they do not allow the decryption of data at the target. Therefore, they are mostly used for traditional authentication systems and are not common in the wireless sensor network context.  
Some authors [7] argue for the use of symmetric encryption in WSN instead of asymmetric encryption, due to the high overhead of computing imposed by asymmetric algorithms. However, some works have shown that it may be feasible to employ asymmetric cryptography in WSN [6; 7].
## 2 Radar Jamming Signal Analysis and Modeling
### Noise Modulation Jamming
#### 2.1.2 Noise Frequency Modulation Jamming  
Noise Frequency Modulation Jamming (NFM) is a form of broadband jamming commonly employed to disrupt frequency-diverse and frequency-agile radar systems.  
\[J(t)=U_{j}\text{exp}\left[j\left(2\pi f_{c}+2\pi K_{FM}\int\limits_{0}^{t}u\left( t^{\prime}\right)dt^{\prime}+\varphi\right)dt\right] \tag{2}\]  
NFMJ is a generalized stationary random process, where \(\text{exp}(\cdot)\) represents the exponential function with base \(\mathbf{e}\). The modulating noise \(\varphi\) is zero-mean Gaussian white noise, uniformly distributed within the range \([0,2\pi]\), and is independent of the signal \(u(t)\). The carrier frequency \(f_{c}\) is the central frequency of the noise frequency modulated signal. The modulation slope \(K_{FM}\) determines the rate of frequency change. The amplitude \(U_{j}\) of the jamming controls the increase or decrease of frequency caused by the intensity of the modulating signal. The effective modulation index \(m_{f}\) can be calculated using the following equation:  
\[m_{f}=K_{FM}/\Delta F_{n} \tag{3}\]  
where \(\Delta F_{n}\) represents the bandwidth of the modulating noise. The effective modulation index is a crucial determinant between coherent jamming and broadband jamming. When \(m_{f}\geq 1\), the noise frequency modulation jamming transitions into broadband jamming; otherwise, it remains a coherent jamming technique.
## 2 Materials and Methods
### Construction of the Cutting Device Model Based on ADAMS
#### 2.3.2 Movement Trajectory Analysis of the Cutting Device  
The moving drive and rotating drive of the cutting device are set at 166.67 mm/s and 3600\({}^{\circ}\)/s, respectively, to simulate the operation where the harvester moves forward at a speed of 0.6 km/h and the cutting device cuts at a speed of 600 r/min. After completing the simulation, the marker function in the ADAMS software is effectively utilized to calibrate the end point of each edge on the cutter, and the trajectory curve of the marker in the entire cutting process is drawn with the ground as the reference (see Figure 7). Figure 7a, for a single-marker movement trajectory, further verifies that the motion path of the cutter represents a simple harmonic curve. Figure 7b, for the two adjacent markers of the motion curve, demonstrates that the curve part of the segment possesses the intersection phenomenon. Figure 7c shows that, for the multiple adjacent markers on the curve, the intersection phenomenon between the curves is more obvious, indicating that there exists a repetitive cutting of the cutting device during the operation course.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Restraint Components** & **Constraint** \\ \hline Left pendulum rod (7) and left rack (8) & Revolute joint \\ Left rack (8) and ground & Translational joint \\ Cutter (1) and long linkage (6) & Parallel joint primitive \\ Cutter (1) and right pendulum rod (2) & Spherical joint \\ Right pendulum rod (2) and long linkage (6) & Revolute joint \\ Cutter (1) and right pendulum rod (2) & Revolute joint \\ Right pendulum rod (2) and right rack (3) & Revolute joint \\ Right rack (3), ground & Translational joint \\ Linkage (4) and right pendulum rod (2) & Spherical joint \\ Crank (5) and linkage (4) & Revolute joint \\ Crank (5) and right rack (3) & Revolute joint \\ \hline \hline \end{tabular}
\end{table}
Table 4: Adding constraints to each component of the cutting device.  
Figure 6: A simulated model utilized for the cutting device: (1) Cutter; (2) Right pendulum rod; (3) Right rack; (4) Linkage; (5) Crank; (6) Long linkage; (7) Left pendulum rod; (8) Left rack.
## 2 Determining \(G\) for I-II Mixed Mode Crack
### Numerical Analysis of 2D Crack
#### 2.1.3 Computational Analysis  
From the output data of the simulation, the energy release rate and stress intensity factors can be obtained corresponding to the required condition. The method developed in the present study is described as follows:  
1. Determine \(K_{I}\) and \(K_{II}\) under arbitrary combined Mode I and Mode II loading conditions with the initial crack.
2. Determine \(J\) for kink cracks repeatedly, varying the kink length from 0.1 mm to 1 mm with four data points (\(a=0.1\) mm, 0.3 mm, 0.5 mm, 1 mm). Then, compute energy release rate as the kink propagation vanishes on the curve of \(J\) versus kink length by the method of fitting.
3. Plot the non-dimensional value (\(K_{I}/\sqrt{EG},\ K_{II}/\sqrt{EG}\)) for each kink angle (\(\theta=30^{\lx@math@degree}\), \(60^{\lx@math@degree}\), \(90^{\lx@math@degree}\), \(120^{\lx@math@degree}\), \(150^{\lx@math@degree}\))
4. Determine the parameters of inclined ellipses that fit the above data points using the curve fitting algorithm by MATLAB, and the corresponding ellipse equation is presented as follows: \[\frac{\left(\frac{K_{I}}{\sqrt{EG}}cos\alpha+\frac{K_{II}}{\sqrt{EG}}sin \alpha\right)^{2}}{a^{2}}+\frac{\left(\frac{K_{II}}{\sqrt{EG}}cos\alpha-\frac{ K_{I}}{\sqrt{EG}}sin\alpha\right)^{2}}{b^{2}}=1\] (3) where \(a\), \(b\), \(\alpha\) are the semi-major axis, semi-minor axis and inclination of ellipse, respectively.
5. Obtain the coefficients of quadratic of energy release rate in terms of stress intensity factors, and defined as \[G=\frac{1}{E}\Big{(}a_{11}K_{I}^{2}+a_{12}K_{I}K_{II}+a_{22}K_{II}^{2}\Big{)}\] (4)  
As shown in Figure 5, the non-dimensional value (\(K_{I}/\sqrt{EG},\ K_{II}/\sqrt{EG}\)) for each kink angle can be calculated through the technique proposed above. By fitting the database, a series of inclined ellipses are presented with a certain angle and size. Figure 6 shows the inclined angle \(\alpha\) of Iso-energy release rate ellipses with kink angle \(\theta\). The variation of inclination for ellipses drops linearly with the increase of the kink angle. The trend of semi-major axis and semi-minor axis of ellipses according to the variation of kink angle has been shown in Figure 7. The coefficients of quadratics for energy release rate in terms of stress intensity factors are calculated for each kink angle based on the ellipse equations (Figure 8).  
Figure 5: Iso-energy release rate ellipses on normalized mixed mode stress intensity factors.  
Figure 8: Coefficients of energy release rate.  
Figure 6: Inclined angle of the Iso-energy release rate.  
Figure 7: Semi-major and semi-minor axis of the Iso-energy release rate.
# Numerical Study of Endwall Modification with Micro-Scale Ribs in a Turbine Cascade
###### Abstract  
A novel modification method, the'micro-scale' rib, is proposed to expand cooling coverage for turbine endwalls. However, the introduction of the rib will inevitably affect the flow in the near-wall region. Therefore, the variation in the flow pattern for the traditional model of secondary flow needs further exploration. In this paper, to gain a clearer understanding of the micro-scale rib, the original endwall and three types of ribbed endwalls were adopted to numerically present the detailed flow, film cooling, and heat transfer information for the endwall surface and phantom cooling on the suction side (SS) of the blade. The Ansys code CFX was utilized to solve the 3D Reynolds-averaged Navier-Stokes (RANS) equations, and the SST k-\(\omega\) was selected as the turbulence model after the verification. The results show that the rib-like vortex changed the flow of the coolant and had various impacts on the cooling characteristics. Although the cooling performance of the ribbed endwall improved, it also had a negative impact on heat transfer in most cases. Compared with the original, the vertical rib cases provided optimal film cooling, with increases of 26.9% and 17.4% for rib spacing values of 8 mm and 10 mm, respectively, with little difference in heat transfer (less than 1%). In addition, the horizontal rib cases presented the worse performance for both film cooling and heat transfer, which indicates that the rib layout should consider a mainstream flow direction for future designs.  
turbine endwall; endwall modification; micro-scale rib; film cooling; heat transfer +
Footnote †: journal: Applied sciences
## 3 Formulation and Solution of Governing Equations  
### Fundamental Equations  
For horizontal transient vibrations, assuming negligible vertical displacements of both the soil and pore water, the three-dimensional equation of motion for saturated soil in a cylindrical coordinate system under non-axisymmetric conditions is expressed as [26]:  
\[G_{i}\
abla^{2}u_{ri}+(\lambda_{i}+G_{i})\frac{\partial e_{i}}{\partial r}- \frac{G_{i}}{r^{2}}\bigg{(}2\frac{\partial u_{ri}}{\partial\theta}+u_{ri} \bigg{)}-\alpha\frac{\partial p_{f}}{\partial r}+G_{i}\frac{\partial^{2}u_{ri }}{\partial z^{2}}=\rho_{i}\ddot{u}_{ri}+\rho_{f}\ddot{w}_{ri} \tag{1}\]  
\[G_{i}\
abla^{2}u_{oi}+(\lambda_{i}+G_{i})\frac{1}{r}\frac{\partial e_{i}}{ \partial\theta}-\frac{G_{i}}{r^{2}}\bigg{(}u_{oi}-2\frac{\partial u_{ri}}{ \partial\theta}\bigg{)}-\alpha\frac{1}{r}\frac{\partial p_{f}}{\partial\theta }+G_{i}\frac{\partial^{2}u_{oi}}{\partial z^{2}}=\rho_{i}\ddot{u}_{oi}+\rho_{f }\ddot{w}_{oi} \tag{2}\]  
The governing equation for fluid motion in the soil is:  
\[-\frac{\partial p_{f}}{\partial r}=\frac{1}{k_{d}^{\prime}}\ddot{w}_{ri}+\rho _{f}\ddot{u}_{ri}+\frac{\rho_{f}}{n}\ddot{w}_{ri} \tag{3}\]  
\[-\frac{1}{r}\frac{\partial p_{f}}{\partial\theta}=\frac{1}{k_{d}^{\prime}} \ddot{w}_{oi}+\rho_{f}\ddot{u}_{oi}+\frac{\rho_{f}}{n}\ddot{w}_{oi} \tag{4}\]  
Figure 1: Schematic diagram of the analytical model. **(a)** Pile–soil interaction system; **(b)** flexible support at the pile head.  
The governing equation for soil seepage is:  
\[M_{i}\left(\frac{\partial\dot{w}_{ri}}{\partial r}+\frac{\dot{w}_{ri}}{r}+\frac{1 }{r}\frac{\partial\dot{w}_{\theta i}}{\partial\theta}\right)+\alpha_{i}M_{i} \left(\frac{\partial\dot{u}_{ri}}{\partial r}+\frac{\dot{u}_{ri}}{r}+\frac{1}{r }\frac{\partial\dot{u}_{\theta i}}{\partial\theta}\right)=-\dot{p}_{f} \tag{5}\]  
In the equation,  
\[k_{di}^{\prime}=k_{di}/\left(\rho g\right),\ \
abla^{2}=\partial^{2}/\partial r^{2}+ \partial/(r\partial r)+\partial^{2}/\left(r^{2}\partial\theta^{2}\right),\]  
\[e_{i}=\partial u_{ri}/\partial r+u_{ri}/r+\partial u_{\theta i}/r\partial\theta, \ K_{bi}=\lambda_{i}+(2/3)\times G_{i},\ \alpha_{i}=1-K_{bi}/K_{si},\ M={K_{si}}^{2}/(K_{di}-K_{bi}),\]  
\[K_{di}=K_{si}[1+n_{i}(K_{si}/K_{fi}-1)],\ \rho_{i}=(1-n_{i})\rho_{si}+n_{i} \rho_{fi}.\]  
The shear effects must be considered when analyzing the impact response of structures [27; 28]. Consequently, the Timoshenko beam model, which accounts for shear deformation in the pile body, was employed in the following analysis. The governing differential equations of motion for this model are expressed as follows:  
\[k^{\prime}G_{p}A\left(\frac{\partial\theta}{\partial z}-\frac{\partial^{2}u}{ \partial z^{2}}\right)+\rho_{p}A\frac{\partial^{2}u}{\partial t^{2}}=-(q_{n1} +q_{n2}) \tag{6}\]  
\[k^{\prime}G_{p}A\left(\theta_{p}-\frac{\partial u_{p}}{\partial z}\right)+ \rho_{p}I_{p}\frac{\partial^{2}\theta_{p}}{\partial t^{2}}-E_{p}I_{p}\frac{ \partial^{2}\theta_{p}}{\partial z^{2}}=0 \tag{7}\]  
In the equation,  
\[k^{\prime}=\frac{3}{4}\frac{r_{1}^{2}+r_{2}^{2}}{r_{1}^{2}+r_{1}r_{2}+r_{2}^{2}}\]
## 3 Results and Discussion
### Cold Mechanical Strength of Biocarbon Briquettes  
The cold compression strength (CCS) was measured for all recipes, as shown in Figure 4. Most of the biocarbon recipes exhibited higher mechanical strength compared to CBB. R1 was spontaneously disintegrated, while the highest values of CCS were demonstrated by R8 and R10, which was higher than that of R0. This indicated that molasses with lime can provide an adequate mechanical strength for biocarbon biriquettes. R11 and R13-R15 showed similar values of CCS to that of R0, which indicated that 25% of charcoal can be added to coke breeze without deteriorating the mechanical strength of the briquettes.  
The splitting tensile strength (STS) was measured for all the recipes, as shown in Figure 5. The STS for CBB was not measured as it has a different geometry (hexagonal prism), while the reference and biocarbon briquettes have a cylindrical shape, which allowed for the load to be applied on the peripheral surface of the briquettes. The highest STS were demonstrated by R15, R13 and R10. This indicated that both molasses and Keracoal were superior in terms of the enhancement of the STS of biocarbon briquettes. The comparison between the mechanical strength of R4 (CCS = 66.25 kg/cm\({}^{2}\) and STS = 5.18 kg/cm\({}^{2}\)) and R14 (CCS = 88.37 kg/cm\({}^{2}\) and STS = 6.38 kg/cm\({}^{2}\)) indicated that the briquettes produced from Charcoal 2 exhibited higher mechanical strength compared to that of Charcoal 1. This can be attributed to the higher ash content in Charcoal 2 compared to that of Charcoal 1. The comparison between R8 and R15 indicated that addition of cement deteriorated the CCS but improved STS. No significant difference was found by changing the type of cement from normal Portland cement in R16 to calcium aluminate cement (CA) in R17.  
In general, most of the briquettes that exhibited high CCS, demonstrated relatively higher STS. The significant difference between the CCS and the STS for all recipes can be attributed to the effect of the applied pressure on the area under load. In case of CCS, the load is distributed on the whole surface area of the briquettes, while in the case of splitting tensile strength, the pressure is applied on a narrow plane in the briquettes. This effect must be considered if the briquette geometry (shape, size and dimension) is changed and the measured strength can be used qualitatively rather than quantitatively to evaluate the large-scale biocarbon briquettes. Similar differences in strength of cylindrical briquettes were observed in the work by [PERSON] et al. [11].  
Figure 3: Average dry density of developed briquettes R0–R27.
## 3 System Model
### PSO Model Review
#### 3.4.6 The Difference between PSO and WKNN  
The WKNN algorithm is a common positioning matching method, but its matching references are all similar fingerprints in the fingerprint database. These fingerprints need to be collected manually. The PSO algorithm used in this study matches the particle coordinates generated by PSO as the reference during the positioning process, which reduces the stringent requirements for fingerprint collection, and accurate positioning can  
Figure 5: A flowchart of the PSO algorithm.  
be completed even if the positioning area is not completely covered by references. The difference between PSO and WKNN is illustrated in Figure 6. As shown in Figure 6a, the k positioning points of the traditional WKNN positioning reference are all dependent on points in the fingerprint database. Since the reference points of the fingerprint database do not cover all areas, the localization may be less accurate in some regions. As illustrated in Figure 6b, the k positioning points of the PSO algorithm are all generated by PSO. These k positioning points can cover all areas, and the positioning accuracy is improved compared to WKNN. The particles generated by the PSO model can cover a denser area compared to the directly sampled fingerprints used in traditional WKNN. This denser coverage allows the PSO model to achieve a more accurate preliminary positioning result.