esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementing CAD API Automated Processes in Engineering Design: A Case Study Approach
## Appendix A
### Limitations and Applicability  
The system developed through this research offers significant potential for integration into a wide range of applications across various sectors, contingent upon the complexity of the tasks involved. Its modular design enables customization, meaning that it can be adapted to meet the specific needs of different industries, provided the appropriate data are available for each application.  
One of the key strengths of this system is its flexibility, which allows it to be employed in various engineering fields, from mechanical design to manufacturing processes, as long as the necessary engineering methodologies and data are available. The code's modular nature ensures that it can be extended or modified as required, making it adaptable to changing needs or advancements in technology. However, the effectiveness and applicability of the system are ultimately dependent on the skills and expertise of the programmer who adapts and configures the system for specific tasks or environments.  
While the system is versatile, it is essential to note that its success is also influenced by the quality and accuracy of the input data and the specific engineering processes defined by the user. Thus, the limitations of the system are primarily determined by the boundaries set by the user and the programming implementation, as opposed to inherent constraints within the system itself.  
If we were to identify an inherent limitation of the code, it would be that adding all the necessary dimensions automatically takes more programming time than manually inserting them into a drawing. In fact, the process of adding dimensions automatically is equivalent to inserting them manually in terms of time and effort. However, this limitation becomes less significant in scenarios where a company frequently handles variations of the same product. For example, a furniture manufacturer who produces the same bolts in different sizes or drawers in various lengths would benefit from automating the dimensioning process, as the time investment in programming would be offset by the efficiency gained across numerous variations. This highlights the system's applicability for companies dealing with product variations, further extending its potential to a broader range of industries.
## 2 Related Work
### Research Gap  
A significant gap in the literature is the integration of IoT connectivity into the user plane of the SDN architecture [12]. Whilst recent studies have focused on SDN optimisation for 5G VANETs via the control plane [14], our proposed framework explores optimisation via the user plane, achieving a consistent packet delivery rate of 100% for connected vehicles. Furthermore, whilst AI frameworks integrating ML into SDN Controller APIs offer greater flexibility in resource management, these frameworks strongly rely on large datasets and reliable models capable of guaranteeing the URLLC-class, particularly for critical vehicular services. To ensure the URLLC-class guarantees for critical vehicular services, it is imperative that operations such as real-time traffic classification are 100% accurate, as demonstrated in our study.
## 3 Results
### The Analysis of the Conditions for Gamma-Decalactone Biosynthesis
#### 3.1.2 Effect of Heptane Presence on Gamma-Decalactone Production  
The decrease in the concentration of GDL after a certain cultivation time (Figure 1) may have been related, as mentioned, to the use of lactone as a carbon source by yeasts, or it may have resulted from lactone hydrolysis. In either case, the contact of the reactants with the aqueous phase seemed to be decisive for the course of the process. Hence, an attempt was made to conduct a cultivation with the addition of an organic solvent: heptane  
Figure 1: Effect of castor oil concentration in the medium on the amount of synthesized GDL.  
(1 mL/100 mL). The aim of this experiment was to examine whether the presence of an organic phase in which GDL could dissolve and be less accessible to microorganisms, and at the same time less susceptible to hydrolysis, would limit the decrease in its concentration in the medium. Heptane was chosen as a solvent due to its relatively low toxicity and neutrality to the growing yeasts _Yarrowia lipolytica_[33].  
The results of the experiment (Figure 2) for two castor oil concentrations in the medium, 50 and 100 g/L, did not show a clear correlation between the addition of heptane and the maximum amount of GDL synthesized by yeast. In the cultures with the addition of heptane, as in the control sample, maxima of the reaction were observed, after which the lactone concentration decreased.
# Real-time Conflict Resolution Algorithm for Multi-UAV Based on Model Predict Control
## 3 Modeling of the Conflict Resolution Problem  
### Modeling of UAV  
Supposing that all the UAVs are flying in the coordinate system of ground (_Oxyz_), which can be defined as: O is the selected coordinate origin, _Ox_ points to true north, _Oz_ axis points to true east, and _Oy_ axis is determined by the right-hand rule; and all UAVs move within the horizon plane _Oxz_. So that the UAV can be regarded as [PERSON] vehicle [PERSON] (1988), then by discrete sampling of the flight process of each UAV at the _k_th time, the flight state of UAV can be defined as \(\mathrm{x}(k)=(x(k),z(k),\psi(k))\). The discretized kinematic equations of UAV can be given as:  
\[\left\{\begin{array}{c}x(k+1)-x(k)=v(k)\Delta t[\sin\psi(k+1)-\sin\psi(k)]/ \omega(k)\\ z(k+1)-z(k)=v(k)\Delta t[\cos\psi(k)-\cos\psi(k+1)]/\omega(k)\\ \psi(k+1)-\psi(k)=\omega(k)\Delta t\end{array}\right. \tag{1}\]  
where, \(\psi\) is the heading angle of UAV, \(\omega\) is heading angular speed, \(\Delta t\) is the sampling interval.  
Rewrite (1) into a matrix form:  
\[\left\{\begin{array}{c}\mathbf{x}(k+1)=\mathbf{A}(k)\mathbf{x}(k)+\mathbf{B }(k)\mathbf{u}(k)\\ \mathbf{y}(k+1)=\mathbf{C}(k+1)\mathbf{x}(k+1)\\ \mathbf{u}(k)=[v(k),\omega(k)]^{\mathrm{T}}\end{array}\right. \tag{2}\]  
where, \(\mathbf{y}\) represents for the output vector of UAV, matrixes \(\mathbf{A}\), \(\mathbf{B}\) and \(\mathbf{C}\) satisfy the equations below:  
\[\mathbf{A}(k)=\mathbf{C}(k)=\mathbf{e}_{3\times 3};\;\;\;\mathbf{B}\;\;(k)= \left[\begin{array}{cc}\frac{[\sin\psi(k+1)-\sin\psi(k)]}{\omega(k)}&0\\ \frac{[\cos\psi(k+1)-\cos\psi(k)]}{\omega(k)}&0\\ 0&\Delta t\end{array}\right] \tag{3}\]  
The constraints of this problem are mainly composed of UAV flight performance constraints and collision avoidance constraints:  
(1) UAV flight performance constraints \[\left\{\begin{array}{c}0\leq\left|\omega(k)\right|\leq\omega_{\max}\\ 0\leq\left|v(k)\right|\leq\mathrm{n}_{\max}\\ v_{\min}\leq\left|v(k)\right|\leq v_{\max}\\ r_{turn}=\frac{v_{\min}}{\omega_{\max}}\leq\left|\frac{v(k)}{\omega(k)}\right| \end{array}\right.\] (4) where, \(v_{\max}\), \(n_{\max}\), \(\omega_{\max}\) represent for the maximum speed, overload, angular speed of UAV, \(r_{turn}\) represents for the minimum turning radius of UAV.
2. Collision avoidance constraint Denote the safe flight radius of UAV as \(r_{safe}\), if the distance between any two UAVs (\(r_{ij}\)) is less than \(r_{safe}\), these two UAVs are considered to collide [PERSON] (1988). So at _k_th time, the collision avoidance constraint can be given as below: \[r_{ij}(k)=\sqrt{\left[x_{i}(k)-x_{j}(k)\right]^{2}+\left[z_{i}(k)-z_{j}(k) \right]^{2}}\geq r_{safe}\] (5) \[(\forall i,j=1,2,3,\ldots,N_{V})\]
# Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance
## 2 Obstacle Avoidance for Dynamic Movement Primitives
As we mentioned before, the strength of potential filed is largely determined by \(\lambda\) and the effect of \(\lambda\) is evident on movement generation. Therefore, different \(\lambda\) lead to different behavior in obstacle avoidance. By setting different values of \(\lambda\), there is different performance in obstacle avoidance as shown in Figure 2. It is not difficult to find that, if \(\lambda\) is set to a smaller value such as \(\lambda=0.003\) (see the orange curve in Figure 2), the generated movement may fail in an obstacle avoidance; if \(\lambda\) is set to a bigger value such as \(\lambda=0.1\) (see the purple curve in Figure 2), the movement has a bad performance in tracking the desired trajectory. Choosing an appropriate \(\lambda\) is important for generating a better movement (see the green curve in Figure 2). In other words, it is very essential to choose a better potential field for obstacle avoidance and good tracking performance. To our knowledge, the profiles of the generated movement with DMPs are determined not only by the obstacle avoidance repulsive term but also by the parametrized nonlinear term. To this end, if we want to obtain a trajectory with good performance in both obstacle avoidance and trajectory tracking, the parameters \(\theta\) of the DMPs and the strength \(\lambda\) of the potential are optimized simultaneously in the movement generation process. This could be regarded as a high-dimensional optimization problem obviously. Reinforcement learning algorithms are able to optimize motion primitive parameters efficiently and robustly in high-dimensional problems [19]. Thus, we will present a framework based on RL to optimize the parameter of DMP and the strength of the repulsive potential field simultaneously in the next section. Figure 1: The diagram of the steering angle \(\alpha\).
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 2 Basic Principles  
### CEEMDAN Decomposition Algorithm  
The CEEMDAN algorithm is a decomposition algorithm proposed for the shortcomings of EMD and EEMD, which decomposes the original signal into multiple intrinsic mode functions by adding adaptive white noise and iterative decomposition of residual signals, and better solves the problems of mode aliasing and white noise residuals. The specific steps are as follows:  
1. Add \(i\) (\(i=1,2,\ldots,m\)) times of Gaussian white noise \(n^{i}(t)\), obeying the standard normal distribution of the original signal \(y(t)\), and obtain the new signal \(y^{i}(t)\). \[y^{i}(t)=y(t)+n^{i}(t)\] (1)
2. The EMD decomposition of \(y^{i}(t)\) is performed, the first modal component is retained and a mean-taking calculation is performed to obtain the \(I_{IMF1}\): \[I_{IMF1}=\frac{1}{m}\sum_{i=1}^{m}I_{IMF1,i}\] (2)The residual component at this point is \(R_{1}(t)\): \[R_{1}(t)=y(t)-I_{IMF1}\] (3) 3. Adding \(i\) (\(i=m\)) times Gaussian white noise \(n^{i}(t)\), obeying standard normal distribution, to the residual component \(R_{1}(t)\) yields \(R_{1}^{i}(t)\): \[R_{1}^{i}(t)=R_{1}(t)+n^{i}(t)\] (4) and EMD decomposition is performed to retain the first modal component, and the expression \(I_{IMF2}\), after taking the mean value, is obtained: \[I_{IMF2}=\frac{1}{m}{\sum\limits_{i=1}^{m}{I_{IMF_{2,1}^{i}}}}\] (5) 4. Repeat the above steps \(j\) (\(j=n\)) times until the residual component cannot be decomposed. \(I_{IMF1}\), \(I_{IMF2}\),..., \(I_{IMFn}\) and residual components are obtained sequentially. The original signal can be expressed as: \[y(t)=\sum\limits_{j=1}^{n}{I_{IMFj}-R_{n}(t)}\] (6)
# The Development of Current Collection in Micro-Tubular Solid Oxide Fuel Cells--A Review
## 5 Materials for Current Collection
### Historical Trend in SOFC Interconnect Materials  
The trend in interconnect material selection, following the movement from high-temperature SOFC operation above 850 \({}^{\circ}\)C to lower temperatures around 700 to 750 \({}^{\circ}\)C, has replaced the traditionally used ceramic perovskite-type materials such as LaCrO\({}_{3}\), as used in the original Westinghouse tubular designs, by nickel- or chromium-based steels and more recently ferritic stainless steels, as seen in Figure 7[6; 61]. Ceramic-based interconnects are brittle, costly and bring difficulties concerning their manufacture, shaping and cell assembly [62]. Metallic interconnects, however, are less brittle, potentially lower in cost, and possess inherent ductility, lending themselves to facile shaping. Metallic interconnects also have a much higher thermal conductivity than the traditional ceramic counterparts which leads to a favourable reduction in start-up/shut-down times of stacks, widening the scope for potential SOFC applications [6]. Typical metallic interconnect materials include ferritic stainless steels such as Crofer 22A/APU/H and ITM, chromium-based materials such as CFY [63], austenitic steels such as Alloy 310 [64] and nickel-based alloys such as Haynes 230, favoured for its corrosion resistance over Inconel and Hastelloy counterparts [65; 66; 67].  
Figure 7: Trend in R&D of SOFC interconnect materials [68].
## 1 Introduction
[16] proposed guiding principles for model merging, emphasizing the importance of using models with the same pre-training starting point, preferring simpler models with consistent performance, and favoring full-model merging over partial-layer merging. [PERSON] et al. [17] developed a unified architecture capable of processing four modalities--image, video, audio, and speech--by converting tasks into a \"sequence-to-sequence\" format, enabling the use of a unified feature extractor and classifier across all modalities. While these approaches show promise, one of the key challenges in model fusion remains to select the appropriate parameters during the fusion process, as different parameter configurations may be advantageous for different tasks. As a result, developing a sustainable framework for advancing model fusion in specific domains is still an ongoing area of research. This paper explores the challenges of searching a high-dimensional parameter space during model fusion, as well as task capability enhancement issues. A large language model fusion method based on Reinforcement Learning [18] for task enhancing (RL-Fusion) is proposed. The core innovation of RL-Fusion lies in its use of reinforcement learning to dynamically guide the selection of fusion parameters, enabling an intelligent and adaptive exploration of the parameter space to identify optimal combinations. Additionally, RL-Fusion introduces a novel dynamic evaluation mechanism that adjusts the evaluation dataset in real time based on feedback from large language model evaluations, ensuring continuous refinement and enhancement of task-specific performance. This method identifies the optimal combination of fusion parameters to maximize model performance, particularly in enhancing performance for specific tasks, while preserving the model's original capabilities. Performance evaluation metrics on datasets such as MMLU [19], C-eval [20], and Yayi [21] demonstrate superior results compared to the base models. The main contributions of this work are outlined as follows:  
1. Reinforcement learning-based model fusion is employed to optimize parameter selection during fusion by rewarding the results, which allows for effective exploration of optimal parameter combinations and enhances model performance. 2. The evaluation feedback module of the LLM provides real-time dynamic feedback, adjusting the evaluation dataset according to the reinforcement learning results, thereby effectively enhancing the fusion model's performance. 3. Effectiveness testing on multiple datasets demonstrates that the method provides modeling capabilities for task enhancement. The remainder of this paper is organized as follows: Section 2 reviews related work. Section 3 introduces the main steps of RL-Fusion. Section 4 presents the evaluation results of the fused model on multiple datasets. Section 5 concludes the paper and outlines directions for future research.
## 3 Results
#### Forward modeling
The kinematics of the hip and knee were not imposed and resulted from the application of the RF force plus the residual moments (see text). Figures 3 and 5 show that when the RF force is decreased, more time would be required for the foot to reach the ground. Therefore, we allowed the simulation to continue beyond the cycle time without any internal joint moment in order to get the position of the foot (angle and step length) at the foot-floor contact time. We also computed the same parameters in the \"increased RF force\" simulations at the time where the foot was in a proper position to contact the ground. Figure 7 shows the respective foot positions, and Figure 8 reports the step parameters. Figure 8: Changes of step parameters when the RF force was changed (the percentage of RF force is reported on the abscissa). Left panel refers to conditions in which the hip kinematics was imposed; right panel: both the hip and knee kinematics were determined by RF force. From top to bottom: time at initial contact; step length; height of the heel at time 100% of stride cycle; angle of the foot plant in relation to the horizontal line (negative angles mean toes down). Figure 7: Foot position achieved at foot-floor contact when RF force was changed (the numbers show the percentage of RF force). The two columns on the left refer to a condition in which only the knee joint was controlled by the RF force, while the hip joint kinematics was imposed (this corresponded to full compensation by hip muscles); the two columns on the right refer to a condition in which both hip and knee were dependent on RF force. Note that these figures do not report the results of when the RF force reduced to 50%, since the foot did not reach the ground. As well, it was not possible to calculate the step parameters for the RF force above 120%, when both hip and knee were free to move, because they stayed almost fixed, and the foot soon penetrated the ground.
## 2 The Joint Positioning Approach
### Decoherence-Based AOA Positioning Scheme
\begin{split}& R_{\text{FB}}G=\text{APA}^{\text{H}}+\text{e}^{2}G\\ =&[S\ \ \ G]\Sigma[\genfrac{}{}{0.0 pt}{}{\text{S}^{\text{ SH}}}{\text{G}^{\text{H}}}]G=[S\ \ \ G]\Sigma[\genfrac{}{}{0.0 pt}{}{\text{O}}{\text{I}}]=\sigma^{2}G\end{split}  \tag{10}\]  
Therefore, the following can be inferred:  
\[\text{GAPA}^{\text{H}}G=\text{O} \tag{11}\]  
And the sufficient condition for Equation (11) to hold is as follows:  
\[\text{A}^{\text{H}}G=\text{O} \tag{12}\]  
Based on Equations (11) and (12), we have the following:  
\[\text{a}^{\text{H}}(\phi)G=\text{O}^{\text{T}}\ \ \ \phi=\phi_{1},\phi_{2}, \ldots,\phi_{p} \tag{13}\]  
Therefore, the spatial spectrum \(P_{Music}\) of the received signal is as follows:  
\[ \begin{split} P_{Music}(\phi)&=\frac{1}{\text{a}^ {\text{H}}(\phi)\text{GG}^{\text{H}}\text{a}(\phi)}\\ &=\frac{1}{\text{a}^{\text{H}}(\phi)(\text{I}-\text{SS}^{\text{H} })\text{a}(\phi)}\end{split}  \tag{14}\]  
When \(\phi=\phi_{1},\phi_{2},\ldots,\phi_{p}\), the value of \(P_{Music}\) is large; otherwise, the value of \(P_{Music}\) is small. Find the P guiding vectors that make the \(P_{Music}\) obtain an extreme value, where the angle corresponding to \(\mathbf{a}(\phi_{1}),\mathbf{a}(\phi_{2}),\ldots,\mathbf{a}(\phi_{p})\) is the angle of incidence of the interfering signal. Finally, the AOA value for a single monitoring base station is as follows:  
\[\text{A}=\arcsin\frac{\Delta\phi_{12}}{d}\frac{c}{2\pi f_{carr}} \tag{15}\]  
where \(\Delta\phi_{12}\) is the phase difference between two antennas, c is the speed of light, and \(f_{carr}\) is the carrier frequency. Taking two monitoring base stations as an example, the coordinates of the base stations are \((x_{1},y_{1})\) and \((x_{2},y_{2})\). According to the sine theorem, the location of the interference source can be obtained as follows:  
\[\text{X}=\frac{\text{Lsin}\text{A}_{2}}{\text{sin}(\text{A}_{2}-\text{A}_{1} )}\text{cos}\text{A}_{1}+\text{x}_{1}\text{Y}=\frac{\text{Lsin}\text{A}_{2}} {\text{sin}(\text{A}_{2}-\text{A}_{1})}\text{sin}\text{A}_{1}+\text{y}_{1} \tag{16}\]  
where L is the distance between two monitoring base stations. When there are more than two monitoring base stations, the coordinate equations may be non-linear, and the least squares method can be used to solve the coordinate optimal solution, which will be introduced later.
## 3 Results
### Accuracy of the Photogrammetric Intersection  
The photogrammetric central projection is a projection of the three-dimensional object space (\(X\), \(Y\), \(Z\)) onto a two-dimensional image plane (\(x\), \(y\)) (Figures 1 and 2). The central projection equations (\(X\), \(Y\), \(Z\)) \(\rightarrow\) (\(x\), \(y\)) are given by following [26,27]:  
\[\begin{array}{rl}x=x_{0}-&f_{5}\frac{r_{11}(X-X_{0})+r_{12}(Y-Y_{0})+r_{13}(Z -Z_{0})}{r_{31}(X-X_{0})+r_{32}(Y-Y_{0})+r_{33}(Z-Z_{0})}\\ &-[k_{1}((x-x_{0})^{2}+(y-y_{0})^{2})+k_{2}((x-x_{0})^{2}+(y-y_{0})^{2})^{2}](x -x_{0})\\ &-k_{3}[3(x-x_{0})^{2}+(y-y_{0})^{2}]-2k_{4}(x-x_{0})(y-y_{0})\\ \\ y=y_{0}+&f_{5}\frac{r_{12}(X-X_{0})+r_{22}(Y-Y_{0})+r_{23}(Z-Z_{0})}{r_{33}(X -Z_{0})+r_{32}(Y-Y_{0})+r_{33}(Z-Z_{0})}\\ &-[k_{1}((x-x_{0})^{2}+(y-y_{0})^{2})+k_{2}((x-x_{0})^{2}+(y-y_{0})^{2})^{2}](y -y_{0})\\ &-2k_{3}(x-x_{0})(y-y_{0})-k_{4}[(x-x_{0})^{2}+3(y-y_{0})^{2}]\\ \end{array} \tag{7}\]  
where \(X\), \(Y\), \(Z\) are the coordinates of an object point P; \(x\), \(y\) are the pixel image coordinates of the projected point P; \(Y_{0}\), \(Y_{0}\), \(Z_{0}\) are the coordinates of the central projection point O; \(x_{0}\), \(y_{0}\) are the image pixel coordinates of the central point O; \(k_{1}\), \(k_{2}\), \(k_{3}\), \(k_{4}\), are parameters of the camera distortion, \(f\)s =\(f\)/s, f is the focal length of the camera, s is the CCD cell size of the camera, whilst:  
\[\left[\begin{array}{ccc}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\\ \end{array}\right]=\left[\begin{array}{ccc}\cos\phi\cos\kappa&\cos\omega \sin\kappa+\sin\omega\sin\phi\cos\kappa&\sin\omega\sin\kappa-\cos\omega\sin \phi\cos\kappa\\ -\cos\phi\sin\kappa&\cos\omega\cos\kappa-\sin\omega\sin\phi\sin\kappa&\sin\omega \cos\kappa+\cos\omega\sin\phi\sin\kappa\\ \sin\phi&-\sin\omega\cos\phi&\cos\omega\cos\phi\\ \end{array}\right] \tag{9}\]  
is the matrix of rotation (\(\omega\), \(\varphi\), \(\varkappa\)) of the object reference frame (\(X\), \(Y\), \(Z\)) related to the camera reference frame (\(Y_{k}\), \(Y_{k}\), \(Z_{k}\)). The frame's origin (\(Y_{k}\), \(Y_{k}\), \(Z_{k}\)) is the central projection point O, the axes \(Y_{k}\), \(Y_{k}\) run parallel to the sections of the CCD frame, and \(Z_{k}\) is perpendicular to the CCD frame. The six parameters \(Y_{0}\), \(Y_{0}\), \(Z_{0}\), \(\omega\), \(\varphi\), \(\varkappa\) define the external orientation of the camera reference frame (\(Y_{k}\), \(Y_{k}\), \(Z_{k}\)) concerning the object reference frame (\(X\), \(Z\), \(Y\)). The three parameters \(x_{0}\), \(y_{0}\), \(f\) = \(f_{s}\)s define the internal orientation of the image referring to the camera reference frame (\(Y_{k}\), \(Y_{k}\), \(Z_{k}\)).