esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks
## 4 Experimental Results
### Performance Evaluation of WCRNN against State-of-the-Art Neural Network Training Algorithms
#### 4.1.2 German Credit Approval Problem  
The German credit approval dataset contained all the details concerning approved or rejected credit card applications in Germany. This imbalanced dataset was constituted by 1000 instances (300 negative decisions and 700 positive decisions), with 20 explanatory variables (seven continuous and 13 categorical) The interesting thing about this classification problem is that the data varied and had a mixture of attributes, which were continuous, nominal with small numbers of values, and nominal with larger numbers of values. The network architecture consisted of one hidden layer with 30 neurons and an output layer of two neurons, while the error goal was set to \(E_{G}\leq 0.1\) within the limit of 1000 epochs.  
Figure 2a,b presents the performance profiles for the German credit approval classification problem, based on \(F_{1}\)-score and accuracy. Firstly, it is worth noting that WCRNN\({}_{1}\) and WCRNN\({}_{2}\) outperformed the classical training algorithms, presenting the highest probabilities of being the optimal solvers, relative to both performance metrics. Regarding the \(F_{1}\)-score metric, WCRNN\({}_{2}\) exhibited the best performance, outperforming the rest of training algorithms, followed by WCRNN\({}_{1}\). Furthermore, WCRNN\({}_{1}\) and WCRNN\({}_{2}\) reported 28% and 44% of simulations with the highest classification accuracy, respectively; while Rprop, LM, and SCG reported 26%, 14%, and 18%, respectively. Thus, the interpretation of Figure 2 demonstrates that the application of the bounds on the weights of the neural network increased the overall classification accuracy.  
Figure 1: Log\({}_{10}\)-scaled performance profiles for the bank management classification problem. The performance profile plots, for every \(\tau\geq 1\), the proportion \(P(\tau)\) of simulations for which any training algorithm has a performance within a factor \(\tau\) of the best algorithm. (**a**) \(F_{1}\)-score. (**b**) Accuracy.
## 6 Further Discussion on Current-Sharing Performance  
In this section, to further demonstrate the current performance of the proposed LED driver, the number of LEDs for all the LED strings are not identical, e.g., three LEDs for \(LS_{1}\), one LED for \(LS_{2}\), three LEDs for \(LS_{3}\), two LEDs for \(LS_{4}\), four LEDs for \(LS_{5}\), and three LEDs for \(LS_{6}\). The waveforms shown in Figures 19-22, based on the PSIM software, are obtained under the rated LED current of 350 mA. Figure 19 shows the gate driving signal for \(Q_{1}\), \(v_{gs}\), the voltage on \(Q_{1}\), \(v_{ds}\), and the current in \(L\), \(i_{L}\). Figure 20 shows the voltage across \(LS_{1}\), \(V_{o1}\), the current in \(LS_{1}\), \(i_{LS1}\), the voltage across \(LS_{2}\), \(V_{o2}\), and the current in \(LS_{2}\), \(i_{LS2}\). Figure 21 shows the voltage across \(LS_{3}\), \(V_{o3}\), the current in \(LS_{3}\), \(i_{LS3}\), the voltage across \(LS_{4}\), \(V_{o4}\), and the current in \(LS_{4}\), \(i_{LS4}\). Figure 22 shows the voltage across \(LS_{5}\), \(V_{o5}\), the current in \(LS_{5}\), \(i_{LS5}\), the voltage across \(LS_{6}\), \(V_{o6}\), and the current in \(LS_{6}\), \(i_{LS6}\), since the voltage across each LED is assumed to be 3.45 V at rated current. Accordingly, based on (10), the calculated duty cycle, close to the simulated value shown in Figure 19, is  
\[D=\frac{\frac{\frac{1}{3}\sum\limits_{m=1}^{6}V_{om}-V_{in}}{\frac{1}{3}\sum \limits_{m=1}^{6}V_{om}}}{\frac{1}{3}\sum\limits_{m=1}^{6}V_{om}}=\frac{18.4-1 2}{1  
In addition, from Figures 19-22, it can be seen that even though the LED counts for individual LED strings are not the same, the currents flowing through all the LED strings are almost identical due to current control and current-sharing interleaved capacitors.
# A Novel Shape-Adjustable Surface and Its Applications in Car Design
## 3 Automobile Body Design with the SQ-Coons Surface  
### Modelling Template  
In order to apply this new surface to automobile modeling design, a CAD shape-adjustable automobile template needs to be generated. The template must meet the following three geometric requirements. Firstly, the surface composition should reflect the current car design characteristics. Furthermore, it should be built entirely from quadrilateral surfaces, which could be replaced by the SQ-Coons surface, in order to make full use of its flexible advantages. Eventually, the template also needs to develop the important advantages of feature lines in the modeling, so as to facilitate designers to adjust the body modeling through surface edges. Meanwhile, the template needs to be partially simplified from the real vehicle type (see [32]), to improve the design and evaluation efficiency.  
In the type analysis phase, more than 50 types of common three-compartment cars were analyzed, and the appearance design and styling features were counted. As shown in Figure 5a, important structural lines (e.g., sections, profiles, and waist lines), defined in [8], of the vehicle from each view were extracted. Sixty boundary curves with the most prominent features were selected for extraction, and the projection of each curve from multiple views finally determined the space model of the curves and the three-dimensional shape of each surface. By neglecting some details such as the rearview mirror, door handle, grille, etc., an automotive model template including 25 sub-surfaces and 156 control points was finally established, as shown in Figure 5b,c.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Surface Type** & \begin{tabular}{c} **Control** \\ **Points** \\ \end{tabular} & \begin{tabular}{c} **Shape** \\ **Adjustable?** \\ \end{tabular} & \
\begin{tabular}{c} **Interpolates** \\ **Boundaries?** \\ \end{tabular} &
\begin{tabular}{c} **Needs Cross-Boundary** \\ **Derivatives?** \\ \end{tabular} \\ \hline SQ-Coons & 12 & Yes & Yes & No \\ Bézier * & 16 & No & Yes & N/A \\ NURBS * & 16 & Yes & Yes & N/A \\ CE-Bézier & 16 & Yes & No & N/A \\ Bilinearly Coons * & 12 & No & Yes & No \\ Bicubically Coons * & 12 & No & Yes & Yes \\ Extended Coons * & 12 & Yes & Yes & Yes \\ \hline \hline \end{tabular}  
* Bicubic surface.  
\end{table}
Table 1: Comparison of geometric characteristics of various types of parametric surfaces.  
Figure 5: _Cont._  
The template qualitatively describes the spatial orientations and positions of the control points and sub-surfaces, which will be described as the SQ-Coons surface in the following sections, in order to exploit the advantages of the new surface type in modelling design.
## 1 Introduction
Based on preliminary empirical and numerical calculations, the possibility of introducing a differential section in a commercially available relay valve was noted, providing an improvement in the performance of the proposed valve with respect to the original, depending on the nature of the forcing. The determination of the flow parameters of the pneumatic components (resistances), including sections of pneumatic brake valves, can be carried out through experimental methods, as well as numerical simulations using computational fluid dynamics (CFDs). The experimental methods for assessing flow rates can be divided into two main groups--direct, using flow meters [14; 15], and indirect, based on recording pressure changes in control volumes [16]. Pressure waveforms subjected to numerical identification using an adopted flow model, for example the lumped method in the MATLAB-Simulink environment [17], allow for the determination of flow characteristics [18; 19], which are the main assessment indicator. In the case of CFD methods, they use 2D or 3D pneumatic drag models and, depending on the software used, generate drag working volumes with the auto selection option (SolidWorks Flow Simulation) [20; 21] or require volume extraction (Ansys Fluent) [22; 23]. After defining the initial and boundary conditions corresponding to the real conditions of the fluid parameters, the internal volumes and the mesh definition, the numerical environment solves the problem based on the Reynolds Average Navier-Stokes(RANS) model [24; 25]. CFDs allow for the estimation of flow parameters but require an investment of time related to the definition of the size, shape, and local compaction of the numerical mesh to achieve results close to those obtained on an expensive laboratory bench. A number of studies validating the quality of developed empirical models with experimental results are available in the available literature [26; 27; 28; 29], but there is a research gap related to the use of CFDs in the evaluation and performance analyses of air brake systems. The purpose of this study is to assess the applicability of CFD calculations in determining the capacity of the inter-chamber flow control opening in the differential section of a trailer air brake valve. The validation of the obtained calculation results was carried out experimentally using the original test bench. The rest of the article is as follows. Section 2 presents the object of the analysis, the extent of the design changes made, and the selection of the differential section nozzles. The methodology for numerical calculations using CFDs (two environments) and experimental testing is described. Section 3 presents the results obtained from the numerical calculations and experimental tests. Section 4 discusses the results obtained. The conclusions are included in Section 5.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 5 Software and Hardware Co-Design-Based Dynamic Information Flow Tracking
in [35] enforced data-flow integrity through an open-source tagged memory architecture based on RISC-V SoC. This work included a hardware extension of RISC-V SoC and static analysis to generate CFG. They combined the in-core design and off-core design to finish tag propagation and tag checking at runtime. Through static analysis, the extended instructions were executed to indicate how to propagate tag between memory locations and registers. Additionally, the custom coprocessor instructions are inserted to inform the coprocessor to check the specified tags.
# Use of Binary Classification in Non-Invasive Load Monitoring
## 6 Test Results
### Comparison of Classifiers  
The identification accuracies of the two classifiers tested, as measured by the _Accuracy_ index, were very similar at approximately 89% (neural classifier: 88.8%; decision trees: 89.1%).  
The introduction of an additional 'do not know' class for the decision-tree-based classifier resulted in an increase in the value of the Precision coefficient--for all devices, its value exceeds 90% (Table 5)--and there was an equalization of its value for individual electricity consumers. Consequently, the reliability of the answers returned by the decision-tree-based classifier is significantly greater (average Precision: 96.8%, which should be considered a very good result) than the reliability of the answers of the neural classifier (88.8%) (Figure 6).  
The values of the Recall indices for both classifiers are similar (Table 5 and Figure 6). The minimum and maximum values of this index are greater for the classifier using neural networks, but the average value is greater for the classifier built on decision trees.  
The \(F1\) index, which is the harmonic mean of the _Precision_ and _Recall_, indicates a slight advantage for the classifier using decision trees; both the extreme values and the mean for this classifier have higher values than those for the classifier built via artificial neural networks.
# A Collocation Method for the Numerical Solution of Nonlinear Fractional Dynamical Systems
## 2 Materials and Methods
### Nonlinear Fractional Dynamical Systems  
Let \(Y(t):\mathbb{R}\to\mathbb{R}^{m}\) and \(F:\mathbb{R}^{m+1}\to\mathbb{R}^{m}\) be real-valued vector functions, and \(Y_{0}\in\mathbb{R}^{m}\) be a real vector. A nonlinear dynamical system with derivative of fractional, i.e., noninteger, order in time can be written as  
\[\left\{\begin{array}{l}{{}_{C}D_{t}^{\beta}Y(t)=F(t,Y(t)),\qquad t>0,\quad 0 <\beta<1,}\\ \\ Y(0)=Y_{0}.\end{array}\right. \tag{1}\]  
where \({{}_{C}D_{t}^{\beta}Y(t)}\) denotes the _Caputo fractional derivative_ with respect to the time \(t\). For a sufficiently smooth vector function \(Y(t)=[y_{1}(t),y_{2}(t),\dots,y_{m}(t)]^{T}\), its Caputo derivative is defined as  
\[D_{t}^{\beta}Y(t):=\left[D_{t}^{\beta}y_{1}(t),D_{t}^{\beta}y_{2}(t),\dots,D_ {t}^{\beta}y_{m}(t)\right]^{T}, \tag{2}\]  
where  
\[D_{t}^{\beta}\,y(t):=\frac{1}{\Gamma(k-\beta)}\,\int_{0}^{x}\frac{y^{(k)}( \tau)}{(x-\tau)^{\beta+1-k}}\,d\tau\,,\quad k-1<\beta<k\,,\quad k\in\mathbb{N }\,,\quad t>0\,, \tag{3}\]  
and \(\Gamma\) denotes the [PERSON]'s gamma function. For details on fractional calculus and fractional derivatives see, for instance, [13; 14; 15; 16].  
The existence, unicity and smoothness of the solution to (1) can be analyzed by transforming the problem into an integral equation [17; 18].
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
###### Abstract  
Video streaming has become extremely widespread, especially with the growing number of users and the spread of mobile devices, along with the increase in the availability and diversity of multimedia applications and communication technologies. Real-time video communication requires awareness of the quality of experience (QoE) to provide customers with a satisfactory service, for example, in smart cities that use video surveillance systems. The quality of service (QoS) is dependent on network performance, which directly affects the QoE. However, reliance on traditional network infrastructure and routing protocols cannot assure QoS. The emergence of software defined networks (SDN) may eliminate current network limitations. Due to SDN's global view and programmability characteristics, such capabilities could help in providing an automated QoS control and management. This paper introduces video streaming adaptive QoS-based routing and resource reservation (VQoSRR), which gives SDN networks the ability to meet video demands and enhance user experience over best effort networks, such as the ones required for video surveillance in smart cities. In order to implement QoS-based routing (QBR), we developed algorithms for calculating routing, installing routing paths in the forwarding devices, and shifting traffic to an alternative path when QoE is violated. As well, we used queuing mechanisms to allocate resources based on the QoE requirements of video streaming. Our results indicate that resource reservation mechanisms combined with QoS-based routing enable effective control over routes and resources. Our framework guarantees the video quality as well. This technique of using video streaming would improve the tools and applications used for smart cities such as surveillance systems for hospitals and civil defense organizations.  
video streaming; software defined networks (SDN); QoS; QoE; QoS-based routing (QBR); resource reservation; video surveillance; smart cities +
Footnote †: journal: _electronics_  
0
Footnote 0: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Control Layer Architecture
### Control Metadata
#### 3.1.1 Context Indicator Measurement  
A _context indicator measurement_ is the local reading of a context indicator taken at a time by the nodes and the controllers (Figure 1a). The controller receives those measurements upon opportunistic contacts with nodes (Figure 1b) and builds an overview of the network based on this information (Figure 1c). In an OppNet, the contact time, bandwidth, and nodes' energy are limited resources, so we have opted to aggregate those measurements (see Section 3.3). Therefore, the context information we consider is this aggregation.
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 2 Related Works
### Lightweight Multi-Focus Image Fusion Networks  
While deep learning-based MFF approaches have shown great promise, their often complex architectures can be computationally demanding, hindering practical application. This has led to an increasing focus on developing lightweight MFF networks. For example, [PERSON] et al. [41] proposed a lightweight scheme that leverages the Laplacian pyramid transform and an adaptive pulse-coupled neural network with local spatial frequency. This approach aimed to improve fusion efficiency and performance by processing fewer sub-images. Similarly, [PERSON] et al. [42] introduced LNMF. By classifying in-focus and out-of-focus regions, LNMF significantly reduces computational complexity and memory consumption. More recently, [PERSON] et al. [43] presented MLNet, a multi-domain lightweight network. MLNet employs discrete cosine transform-based and local binary pattern-based convolutions to effectively capture both frequency and spatial domain features with a minimal parameter count.  
In the field of multi-modal image fusion, which is closely related to the field of MFF, there are also works focusing on lightweight fusion networks. For example, [PERSON] et al. [44] presented a lightweight image fusion algorithm specifically designed for merging visible light and infrared images by utilizing Depthwise Separable Convolution. In the field of medical image fusion, MATFusion [45] proposed a novel lightweight cross Transformer based on a cross multi-axis attention mechanism. They mainly perform as lightweight on the network, and the proposed method not only performs as lightweight on the network but also indirectly allows the use of a lighter network through the transformation of the fusion paradigm.  
Building upon these efforts, our proposed LightMFF also focuses on an ultra-lightweight architecture. Figure 1 presents a comparison between our proposed method and several prominent learning-based existing MFF methods, evaluating both fusion quality and processing efficiency. To the best of our knowledge, the proposed LightMFF is currently the best method with the lowest computational requirements while maintaining good overall performance.
# Linear Programming-Based Non-Probabilistic Reliability Bounds Method for Series Systems
## 4 Numerical Example
### Numerical Example 3  
The wooden roof truss structure of a specific residential building shown in Figure 11 is investigated. Considering the symmetry of the structure, we only analyze bars 1-11. The maximum load limits \(T_{i},i=1,2,\cdots,11\), as well as the loads \(F_{1}\), \(F_{2}\), and \(F_{3}\), are treated as uncertain variables, and their distribution parameters are given in Table  
Without considering the buckling failure, the structure can be regarded as a ten-component series system since it is statically determinate. The performance functions of the structure, whose determination can be found in Appendix A, can be expressed as follows:  
\[\begin{array}{l}g_{1}=T_{1}-(2F_{1}+2F_{2}+F_{3})\\ g_{2}=T_{2}-(\sqrt{5}F_{1}+\sqrt{5}F_{2}+\frac{\sqrt{5}}{2}F_{3})\\ g_{3}=T_{4}-(2F_{1}+2F_{2}+F_{3})\\ g_{4}=T_{5}-\frac{\sqrt{5}}{2}F_{1}\\ g_{5}=T_{6}-(\frac{\sqrt{5}}{2}F_{1}+\sqrt{5}F_{2}+\frac{\sqrt{5}}{2}F_{3})\\ g_{6}=T_{7}-\frac{1}{2}F_{1}\\ g_{7}=T_{8}-(\frac{\sqrt{5}}{3}F_{1}+\frac{2\sqrt{5}}{3}F_{2}+\frac{\sqrt{5}}{ 2}F_{3})\\ g_{8}=T_{9}-(\frac{\sqrt{5}}{3}F_{1}+\frac{2\sqrt{5}}{3}F_{2})\\ g_{9}=T_{10}-(F_{1}+2F_{2}+F_{3})\\ g_{10}=T_{11}-(\frac{2}{3}F_{1}+\frac{4}{3}F_{2})\\ \end{array} \tag{23}\]  
Noting that the internal force of bar 3 is zero, the structure actually only involves ten-performance functions.  
The uncertainty domain can be denoted by the following:  
\[\left[\begin{array}{c}T_{1}-23\\ T_{2}-255\\ T_{3}-23\\ T_{5}-6\\ T_{6}-255\\ T_{7}-3\\ T_{8}-0.0811\\ -0.1962\\ -0.0372\\ -0.0744\\ -0.0354\\ -0.0368\\ -0.0386\\ -0.0372\\ -0.0744\\ -0.0381\\ -0.0394\\ -0.  
For this example, 977 zero design variables have been removed. Table 9 and Figure 12 give the non-probabilistic reliability analysis results of the proposed method, NRBM, and MCS with \(10^{8}\) samples.  
From Table 9 and Figure 12, it can be seen that the LPNRBM has the same lower bound as, but a smaller upper bound than, the NRBM (their maximum relative errors are respectively 3.9143% and 4.5991%). This also indicates the superiority of the LPNRBM over the NRBM.
# Single-Parameter-Tuned Attitude Control for Quadrotor with Unknown Disturbance
## 5 Experiments
### Outdoor Flight Experiment Result  
Two flight tests are shown in this section. The quadrotor platform, the controller, and the flight performance are shown in Figure 13.  
1. Attitude Tracking Performance. In this test, a set of experiments are designed in order to evaluate the tracking performance of the proposed method. No external disturbance is added in this test. The desired attitudes are manually controlled with a remote controller, and the flight log data are presented in Figures 14 and 15. According to Figure 14, the actual attitude can track the desired attitude rapidly and smoothly. Figure 15 shows the attitude error are between four degrees during the test.
2. Disturbance Rejection Performance. A bottle of water is hung on one side of the drone in this test (see Figure 13). Notably, the water bottle keeps swinging during the flight experiment, which can be considered to be a severe unknown disturbance. Figure 16 shows the tracking performance is still good with the bottle swinging. Figure 17 shows that the maximum attitude error is below four degrees even with severe disturbance.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Generation number \(g_{m}ax\) & 36 \\ Initial standard deviation \(\sigma_{m}ax\) & 60 \\ Number of sample points \(\lambda\) & 10 \\ Number of offspring points \(\mu\) & 3 \\ Learning rate of evolution path update \(c\) & 0.1 \\ Learning rate of rank 1 update \(c_{1}\) & 0.2 \\ Learning rate of rank \(\mu\) update \(c_{\mu}\) & 0.5 \\ Time step & 0.01 s \\ Total time of a task & 5 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: CMA-ES training hyper parameters on real platform.  
Figure 13: Outdoor flight experiments.  
Figure 16: Attitude tracking performance of the RL controller with external disturbance.  
Figure 14: Attitude tracking performance of the RL controller.  
Figure 15: Attitude error of the RL controller.