esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Introduction  
### Automatic Control Concepts  
Automatic control is interested in achieving desired performance for dynamic systems [1]. A dynamic system under control is called a plant. The output of the plant is measured with some kind of sensor and compared with the desired output, which is given as reference input. The difference between the desired output and the real output is called error signal and is the main information used in most automatic control systems. The block diagram of a typical automatic control system is shown in Figure 1 below.  
The aim of the controller is to minimize the error signal which is the difference between the reference and the output of the plant. In order to design the controller, a mathematical model of the plant is derived first. Plants generally fall into one of the two main classes: linear and non-linear. Linear plants are easier to control since their mathematical models are linear differential equations, which are much easier to analyze mathematically.  
Disturbance, also known as external disturbance, is a very important concept for automatic control systems. In order to have a practically useful working system, the controller must be robust to external disturbances, especially expected ones. An important parameter of performance is disturbance rejection, which represents the performance of a system under expected disturbances.  
Automatic control systems date back to the 19 th century [2]. During World War I and especially World War II, significant advances were made in automatic control applications. With the advances in computer technology, modern control research gained an acceleration and various types of applications emerged. These applications include motor control [3; 4; 5; 6; 7], robot manipulator control [8; 9; 10; 11; 12; 13; 14], wheeled mobile robot control [15; 16; 17; 18], legged mobile robot control [19; 20], quadcopter control [21; 22], among many others.  
Automatic control under low-disturbance scenarios is a well-studied problem [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. The challenging open areas of further research regard difficult scenarios with high-amplitude disturbances, which drive the system to the limits of its capability.
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 3 Methodology  
### Variational Quantum Circuits (VQCs)  
Variational Quantum Circuits (VQCs) are the unique part of the architecture of quantum recurrent neural networks. The specific VQC components used in this context are structured in three main parts: an encoding layer, a variational layer, and a quantum measurement layer. Figure 1 shows an example of a VQC.  
The left-hand part of the figure is the encoding layer, which contains a Hadamard gate, two angle embedding layers working as \(R_{y}\) and \(R_{z}\) gates in the quantum concept. The middle part is the variational layer (entanglement layer). It is worth mentioning that the number of qubits (in this example is 4), and the number of variational layers (in this example is 2), can be modified to increase the capability to learn the data.
# Pocs-Augmented CycleGAN for MR Image Reconstruction
## 2 Materials and Methods  
### Problem Definition and Notations  
Denote \(y\in C^{N}\) (\(C\) is the complex domain, and \(N\) is the dimension) as the desired image to be restored, which consists of \(\sqrt{N}\times\sqrt{N}\) pixels, where the undersampled k-space data is represented by \(x\in C^{M}\), in which M <<N. Denote the linear operator (undersampling process) with \(\mathcal{F}_{u}\in C^{M\times N}\), which can be further represented by \(\mathcal{F}_{u}=\mathcal{R}\)\(\mathcal{F}\). The image reconstruction problem under the condition of sub-Nyquist sampling is to recover the image \(y\) from \(\mathcal{F}_{u}y\). For 2D image reconstruction, \(\mathcal{F}\in C^{N\times N}\) is the two-dimensional discrete Fourier transform, and \(\mathcal{R}\in C^{M\times N}\) is an undersampling mask used to select signals in the k-space to be sampled. The same concept can be extended to 3D image reconstruction by extending the 2D operators into 3D ones. For a nonlinear undersampling operator, the corresponding image reconstruction process is often formulated to be an optimization problem:  
\[\underset{y}{min}\ \mathcal{S}(x)+\lambda\big{|}x-\mathcal{F}_{u}.y.\big{|}_{ l_{2}}^{2} \tag{1}\]  
where \(\mathcal{S}\) is a regularization term on \(x\), \(\lambda\) is the data fidelity of the noise level of the acquired measurements \(x\)[23], and \(l_{2}\) is the Euclidean norm.  
By using the deep learning reconstruction method, the problem can be defined by Equation (2):  
\[\underset{y}{min}\big{|}y-f_{DL}\big{(}y_{u}\big{|}\theta\big{)}\big{|}_{l_{2}}^{2 }+\lambda\big{|}x-\mathcal{F}_{u}y\big{|}_{l_{2}}^{2} \tag{2}\]  
where \(f_{DL}\) is the forward mapping of the deep learning network with parameters \(\theta\), \(\lambda\) allows the adjustment of the data fidelity, \(y_{u}\) represents zero-filled reconstruction from under the sampled k-space where \(y_{u}=\mathcal{F}_{u}^{H}x\), and superscript \(\mathcal{H}\) is the conjugated transpose operation.  
The final reconstruction image is the output of the deep learning network and can be expressed as in Equation (3):  
\[\hat{y}=f_{DL}\big{(}y_{u}\big{|}\theta,\lambda,\Omega\big{)} \tag{3}\]  
where \(\Omega\) represents the prior information we obtain from the k-space.
## 2 Literature Review
### Overview of PV Power Prediction
#### 2.1.2 Statistical and AI Methods
To allow each iterative unit in RNN to capture dependencies at various time scales adaptively, GRU was first proposed in 2014 [51]. Similar to LSTM, GRU features gate units that control the information flow within the unit, eliminating the need for independent memory cells. Compared to LSTM, GRU is more robust, requires fewer parameters, and trains more quickly [52]. Articles from recent years that combine LSTM and GRU in PV power estimation stand out when the literature is reviewed [53; 54; 55]. CNN, a feed-forward neural network, increases the scalability and training efficiency of the model by reducing the number of parameters that need to be optimized with the parameter-sharing feature [56]. It is observed in the literature that CNN is preferred as a hybrid with other approaches, especially LSTM [57; 58].
## 2 Model Design and Implementation
### Processing the SEMG Signal  
This is an important stage after the base stage of data collection because this stage plays an important role in the accuracy of the model. The SEMG contains some noise that must be removed. So, the signal was processed by removing this noise from the SEMG signal, Figure 5 shows the SEMG signal processing schematic diagram. The fast Fourier transform (FFT) is used to convert the signal from the time domain into the frequency domain, to analyses the frequency content of the signal. The signal is passed through the Butterworth filter, which is a type of low pass filter that cuts off frequencies determined to travel from the FFT to get the smooth SEMG signal.
## 6 Discussion
### Comparative Study  
To better analyze the strengths and weaknesses of the proposed method, the Adam-SAM-TCN method was compared with recent methods. The RMSE and Score values of these methods on the dataset are presented in Tables 4 and 5. As seen in Table 4, it can be observed that all prediction methods performed better on the datasets FD001 and FD003 compared to the datasets FD002 and FD004. Compared to the LSTM [34], CNN-BGRU-SA [35], TaFCN [36], Multi-attention-TCN [37], RCNN-Abi-LSTM [38], and GATA-TCN [39] methods proposed in other studies, the Adam-SAM-TCN method achieved the smallest RMSE values, indicating superior predictive performance in engine life prediction.  
As seen in Table 5, it can be observed that, unlike the RMSE values in Table 4, the method from reference [37] achieved the lowest Score on the FD003 dataset. This is mainly due to Score's penalties for overestimation and underestimation of the RUL. Apart from the FD003 dataset, the prediction method Adam-SAM-TCN obtained smaller Score values compared to the methods from other literature sources.
# Improved Design of LNG Marine Loading Arm Docking Method Based on TRIZ Theory
## 5 Conclusions  
This study used TRIZ to investigate the slow docking speed and low accuracy of LNG marine loading arms and presented a set of improvement schemes. Functional models, causal chain analysis, Su-Field model analysis, and other tools were used to make improvements to traditional LNG marine loading arms, and a method to improve the control mode, structure optimization, and mechanism design was proposed. The main conclusions are as follows.  
Based on the functional model and causal chain analysis, this paper identified problems in the docking process of LNG marine loading arms and found corresponding solutions using the contradiction analysis method.  
Using the Su-Field model, standard solution, and invention principle, three improvement methods were generated and verified: (1) replacing manual operation with machine vision and automatic control; (2) conducting finite element analysis and topology optimization on the marine loading arm to reduce the wind influence and weight; (3) innovative design of the terminal structure of the marine loading arm to increase the docking accuracy.  
This paper's research presents practical and innovative applications of TRIZ in improving the docking method of LNG marine loading arms. In contrast to previous studies, this paper presents a complete set of feasible solutions, and their effects were verified. The proposed scheme can address existing issues with LNG marine loading arms, enabling automatic docking, which is highly feasible and aligned with future development trends.
## 2 The Model
### Model Assumptions  
The model is based on the following key assumptions:  
* The compartment is assumed to be a rectangular parallelepiped with size \(V_{room}=x_{room}y_{room}z_{room}\). It is divided into two zones separated by an interface: a hot zone containing the combustion products, and for certain ventilation conditions, the excess air and unburned fuel, and a cold zone containing fresh air (Figure 1). This assumes that the two zones coexist permanently and that the hot smoke layer is well stratified. At the start of the simulation, the layers are initialized to ambient conditions and the upper layer volume is set to an arbitrary small value of the compartment volume (here, 0.001).
* Temperature, density, pressure, and species concentrations are assumed to be uniform in each zone. However, unlike other zone models such as CFAST, the pressure is different from one zone to the other. Therefore, in the present model, the set of governing equations includes not only the conservation equations of mass and energy,but also a momentum equation that governs the displacement of the smoke interface as a function of the pressure difference between the two zones. Solving this momentum equation leads to a reduction in the time step, imposed by the propagation of acoustic waves (Courant-Friedrichs-Levy or CFL condition), but allows us to follow more accurately the displacement of the interface.
* To model radiation heat transfer in the compartment, the ten-surface model [12] is applied. These ten surfaces, hereafter called wall segments, are the ceiling, four upper walls (i.e., located above the smoke interface), four lower walls (i.e., located below the smoke interface), and the floor (Figure 2). Each wall segment is assumed to be at a uniform temperature. The fire is assumed to radiate uniformly in all directions from a point source at the center of the flame, located at one third of the flame height, given off a fraction \(\chi_{r}\) of the total energy release rate to thermal radiation. The radiation emitted from a wall surface, a gas layer, and the fire is assumed to be grey and diffuse. Radiation transfer through vent openings, doors, etc., is neglected.
* The transient pyrolysis rate for each fuel involved by the fire is prescribed by the user (here, it is deduced from the experimentally measured mass loss rate history), but it may be constrained by the availability of oxygen in the compartment.
* The specific heats at constant volume and pressure, \(c_{v}\) and \(c_{p}\), are assumed to be constant. They are related to the individual gas constant \(R\) and the ratio of specific heats \(\gamma\) by: \(\gamma=c_{p}/c_{v}\) and \(R=c_{p}-c_{v}\).  
The two-zone model is formulated as a set of ordinary differential equations given below, along with the closure relationships.
## 7 Summary and Conclusions  
This paper provides an extensive overview of the configuration of TSN networks, specifically the shaping part. For proper configuration of TSN shapers, the scheduling problem has to be formulated and solved. Different formulation approaches exist in the literature, which makes it difficult to compare the problem resolution algorithms. In this work, we aimed to classify the different approaches and evaluate the different problem formulations. To this extent, we first presented the possible configurations, the traffic categories, and the shaping options. Then, we presented the challenges and the utility of every shaping choice and we suggested a reasonable mapping between traffic categories and shapers. We focused on scheduling of Time-Aware Shaping, which requires solving a network-wide optimization problem. To help build a clear understating of the scheduling task in TSN networks, we surveyed more than 100 solution approaches in order to analyze the evolution of the state of the art. We deeply analyzed and classified the proposed solutions and presented our perspective on future research directions and the challenges related to TSN scheduling. Finally, we conducted a literature-based analysis of the objectives, constraints, and assumptions of the TAS scheduling problem, we analyzed the nondeterminism in the TSN network, and we presented a possible evaluation plan with a structured set of metrics.  
In future work, we aim for a mathematical formulation of the suggested evaluation metrics (i.e., the interleaving problem probability, the slot slope probability, etc.) and a comparison of different algorithms under the same setup. Another important direction that we are focusing on is the study of the control plane design and management operations. Here, the performance of the control plane is mainly determined by the complexity of the configuration synthesizing process which requires solving off the network-wide schedulingproblem and the time to push the configuration to the TSN elements using protocols such as Netconf/Yang. Finally, reconfiguring TSN elements during runtime is another important aspect that we aim to tackle.  
**Author Contributions:** Writing--original draft, H.C.; Writing--review & editing, A.K.; Supervision, A.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Parts of this work have been supported by the Knowledge Foundation of Sweden (KKS) through the Synergi Project AIDA. Additional funding has been provided by the Bavarian State Ministry of Education and Culture, Science and Art through the High-Tech Agenda (HTA).  
**Data Availability Statement:** The data presented in this study is based on the published papers referenced in the bibliography section.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# CWD-Sim: Real-Time Simulation on Grass Swaying with Controllable Wind Dynamics
## 3 Proposed Algorithms
### Deformation of the Grass Model
Specifically, we calculate \(T\), which encapsulates both the direction vector of the wind \(W\) and its magnitude \(F\). The height of the deformed vertex, which we refer to as \(P^{\prime}_{y}\), plays a critical role in this calculation. The effect of the wind changes depending on the height of the blade, and this is captured in the height value. For example, the wind may have a stronger impact on the top of the blade than on the lower base part. Therefore, we use \(P^{\prime}_{y}\) to adjust the strength of the wind translation vector \(T\). Equation (12) describes how these computations are performed. \[T=F.\left(\
abla^{\prime\prime\prime}_{x}\left(P^{\prime}_{y}\right)^{2},- \left|\
abla^{\prime\prime\prime}\right|\left(P^{\prime}_{y}\right)^{2},- \
abla^{\prime\prime\prime}_{y}\left(P^{\prime}_{y}\right)^{2}\right) \tag{12}\]  
Figures 4 and 5 show another comparison between our equation proposed in (12) and the Bezier curve. As we can see, these two curves are almost identical, which proves that our equation can be used to bend the grass blade influenced by wind force. The final step involves updating the vertex positions by applying the wind translation \(T\) to the initial deformed positions \(P^{\prime}\). Transformation of the positions of the vertex positions is facilitated by the model matrix \(M\). As shown in Equation (13), the final position of the vertex, \(P^{\prime\prime}\), is calculated. \[P^{\prime\prime}=M\big{(}(1-\lambda)T+\lambda P^{\prime}\big{)} \tag{13}\]  
where \(\lambda\) is the weighting parameter. The \(\lambda\) is a weighting parameter that represents the degree of effect that wind translation \(T\) and initial deformation \(P^{\prime}\) have on the final position \(P^{\prime\prime}\). When \(\lambda\) is closer to 0, the wind translation \(T\) has more influence on the final position, and when \(\lambda\) is closer to 1, the initial deformation \(P^{\prime}\) has more influence.
# Distribution and Deposition of Cylindrical Nanoparticles in a Turbulent Pipe Flow
## 2 Models and Equations  
### Flow Laden with Cylindrical Nanoparticles  
In practical applications, there are many cases in which the fluid carries solid particles. At this time, to obtain the information of fluid flow and solid particle motion, it is necessary to establish the fluid equation containing solid particles. In the present study, the flow laden with cylindrical nanoparticles in a round pipe is shown in Figure 1. The flow is incompressible and fully developed turbulent. The modified Navier-Stokes with the additional term of cylindrical nanoparticles is:  
\[\frac{\partial\overline{u}_{i}}{\partial t}+\overline{u}_{j}\frac{\partial \overline{u}_{i}}{\partial x_{j}}=-\frac{1}{\rho_{m}}\frac{\partial\overline{p }}{\partial x_{i}}+\frac{\mu}{\rho_{m}}\frac{\partial^{2}\overline{u}_{i}}{ \partial^{2}x_{j}}-\frac{\partial\overline{u^{\prime}}_{i}u^{\prime}_{j}}{ \partial x_{j}}+\frac{\mu_{a}}{\rho_{m}}\frac{\partial}{\partial x_{j}}[ \overline{a}_{ijkl}\overline{x}_{kl}-\frac{1}{3}(I_{ij}\overline{u}_{kl}) \overline{x}_{kl}] \tag{1}\]  
in which \(\overline{u}_{i}\) and \(\overline{p}\) are the mean velocity and pressure, respectively; \(\rho_{m}\) is the mixing density (Equation (2)); \(\mu\) is the fluid viscosity; \(\overline{u^{\prime}_{i}u^{\prime}_{j}}\) is the Reynolds stress; additional viscosity \(\mu_{a}\) is related to the particle concentration, aspect ratio and orientation distribution, and is given by extending [PERSON]'s theory to account for two-body interactions as shown in Equation (2) [17]; \(\overline{x}_{kl}=(\partial\overline{u}_{k}/\partial x_{l}+\partial\overline{ u}_{l}/\partial x_{k})/2\) is the mean rate-of-strain tensor and \(\overline{a}_{kl}\) and \(\overline{a}_{ijkl}\) are the mean second- and fourth-order tensors of particle orientation, respectively, as shown in Equation (3) [18]. The last term on the right hand side of Equation (1) represents the effect of cylindrical nanoparticles on the flow, and the cylindrical nanoparticles are non deformable.  
\[\rho_{m}=(1-\Phi)\rho+\Phi\rho_{f};\ \ \mu_{a}=\frac{4\Phi\lambda^{2}\mu}{3 \ln(1/\Phi)}\bigg{\{}1-\frac{\ln[\ln(1/\Phi)]}{\ln(1/\Phi)}+\frac{0.6634}{\ln( 1/\Phi)}\bigg{\}} \tag{2}\]  
where \(\rho\) and \(\rho_{f}\) are the fluid and particle density, respectively; \(\Phi\) is the particle volume fraction and \(\lambda\) is the particle aspect ratio.  
\[\overline{a}_{ij}=\oint p_{i}p_{j}\overline{\overline{\psi(\mathbf{p})}}\mathrm{d }\mathbf{p},\ \overline{a}_{ijkl}=\oint p_{i}p_{j}p_{k}p_{l}\overline{\psi(\mathbf{p})}\mathrm{d }\mathbf{p} \tag{3}\]  
where \(\mathbf{p}\) is the particle orientation vector; \(p_{i}\) is the unit vector and \(\overline{\psi(\mathbf{p})}\) is the mean probability density function for particle orientation.
## 3 Results
### Prediction of \(ESV\) for the Southern Jiangsu Urban Agglomeration in 2035 Using the PLUS Model
#### 3.5.1 Forecasted Land Use Areas for Various Scenarios in 2035
Under the CDS, compared to the land use in 2020, there are no significant changes in built-up areas, and farmland is not significantly encroached upon, indicating that urban development is not markedly affected and farmland is effectively protected. The areas of forest land and grassland increase in the northern part of Lake Tai, the northern part of the study area, indicating the development of ecological land. The main water bodies such as Lake Tai show no significant changes, while water bodies in the southwest part of the study area decrease and are replaced by farmland. By combining the specific areas of each land type with the analysis of the \"Jiangsu Provincial Territorial Spatial Plan (2021-2035)\", it can be concluded that the CDS, while meeting the basic requirements of relevant policies, balances urban development and provides protection for arable and ecological land. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Area (km2)** & **Farmland** & **Forestland** & **Grassland** & **Water Area** & **Unused Land** & **Built-Up Land** \\ \hline NDS & 14,899.91 & 1315.20 & 0.16 & 5182.06 & 0.24 & 6724.74 \\ FPS & 15,741.67 & 1295.34 & 0.23 & 4684.58 & 0.21 & 6400.28 \\ EPS & 15,413.69 & 1346.72 & 0.43 & 4964.47 & 0.43 & 6396.56 \\ CDS & 15,374.08 & 1670.25 & 0.62 & 4750.12 & 0.31 & 6326.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Projected land use areas under various scenarios. Figure 9: Land use distribution maps under scenario-based simulations.
# Early Ransomware Detection with Deep Learning Models
## 5 Experimental Evaluation
### Ablation Study  
#### 5.7.1 The Effect of Text Representation  
In this section, we assess the effect that textual feature representation has on the scores of the top models described in the previous section. We report the results these models achieved on the PARSEC-500 and PARSEC-5000 datasets with a window size of 7 when 1-hot vectors, FastText vectors, or BERT sentence embeddings were chosen to encode textual features (see Section 3.6 for details).  
Figure 9: Error analysis—PARSEC-500 dataset with the top model (CNN 1-hot OpsRes W = 7 10 eps).  
F1 scores, sensitivity, specificity, and test times for tests appear in Table 7. Full results for all models, dataset features, and all window sizes are available in Appendix A in Appendices A.5 and A.6.  
For both the PARSEC-500 and PARSEC-5000 datasets, 1-hot encoding showed the best performance and indicated that, despite its simplicity, it is highly effective for our task. FastText appears to be the least effective among the tested representations, yielding the lowest F1 scores for both models. This might suggest that FastText's sub-word features and simpler contextual understanding do not capture enough discriminating information for our specific dataset and task.